US20240328296A1

US20240328296A1 - System and method for efficient optimization of hydrocarbon-production well configuration and trajectory using performance versus drilling-cost profiles

Info

Publication number: US20240328296A1
Application number: US18/192,559
Authority: US
Inventors: David Echeverría Ciaurri; Muhammad Munir
Original assignee: Saudi Arabian Oil Co
Current assignee: Saudi Arabian Oil Co
Priority date: 2023-03-29
Filing date: 2023-03-29
Publication date: 2024-10-03
Also published as: WO2024206419A1

Abstract

A method to perform a drilling operation in a field is disclosed. The method includes computing profiles of candidate wells in the field, each profile including points associated with a corresponding well, each point corresponding to a configuration and trajectory of said corresponding well and including a value pair of performance and drilling cost of said corresponding well, generating, based on the profiles, a ranking of the candidate wells, selecting, from the candidate wells and according to the ranking, selected candidate wells, selecting, from the points associated with each selected candidate well, a number of selected points, performing, for each of the number of selected points, reservoir simulation to generate simulation results, comparing the simulation results to a pre-determined criterion to generate a validated point, and performing, based on the configuration and trajectory of the validated point, the drilling operation.

Description

BACKGROUND

Future hydrocarbon production for an oil/gas field may be forecasted using computer models and various optimization algorithms. Precise predictive models are often in practice computationally expensive. Simulation-based optimization usually requires a large number of simulations for problems with nonlinear cost functions, such as the optimization of well location and configuration/trajectory. As a consequence, although the solutions found are acceptably precise, the associated computing cost may be prohibitive even when proxy or approximate models are used. Alternative solutions without running reservoir-flow simulation can be computed much more rapidly at the expense of significantly lower accuracy in the prediction results.

SUMMARY

In general, in one aspect, the invention relates to a method to perform a drilling operation in a field. The method includes computing a plurality of profiles of a plurality of candidate wells in the field, wherein each profile comprises a plurality of points associated with a corresponding well, each point corresponding to a configuration and trajectory of said corresponding well and comprises a value pair of performance and drilling cost of said corresponding well, generating, based on the plurality of profiles, a ranking of the plurality of candidate wells, selecting, from the plurality of candidate wells and according to the ranking, a plurality of selected candidate wells, selecting, from the plurality of points associated with each selected candidate well, a number of selected points, performing, for each of the number of selected points, reservoir simulation to generate a plurality of simulation results, comparing the plurality of simulation results to a pre-determined criterion to generate a validated point, and performing, based on the configuration and trajectory of the validated point, the drilling operation.
In general, in one aspect, the invention relates to a well configuration and trajectory analyzer to facilitate a drilling operation in a field. The well configuration and trajectory analyzer includes a computer processor and memory storing instructions, when executed by the computer processor comprising functionality for computing a plurality of profiles of a plurality of candidate wells in the field, wherein each profile comprises a plurality of points associated with a corresponding well, each point corresponding to a configuration and trajectory of said corresponding well and comprises a value pair of performance and drilling cost of said corresponding well, generating, based on the plurality of profiles, a ranking of the plurality of candidate wells, selecting, from the plurality of candidate wells and according to the ranking, a plurality of selected candidate wells, selecting, from the plurality of points associated with each selected candidate well, a number of selected points, performing, for each of the number of selected points, reservoir simulation to generate a plurality of simulation results, and comparing the plurality of simulation results to a pre-determined criterion to generate a validated point, wherein the drilling operation is performed based on the configuration and trajectory of the validated point.
In general, in one aspect, the invention relates to a system that includes a wellsite in a field for performing a drilling operation and a well configuration and trajectory analyzer comprising a computer processor and memory storing instructions, when executed by the computer processor comprising functionality for computing a plurality of profiles of a plurality of candidate wells in the field, wherein each profile comprises a plurality of points associated with a corresponding well, each point corresponding to a configuration and trajectory of said corresponding well and comprises a value pair of performance and drilling cost of said corresponding well, generating, based on the plurality of profiles, a ranking of the plurality of candidate wells, selecting, from the plurality of candidate wells and according to the ranking, a plurality of selected candidate wells, selecting, from the plurality of points associated with each selected candidate well, a number of selected points, performing, for each of the number of selected points, reservoir simulation to generate a plurality of simulation results, and comparing the plurality of simulation results to a pre-determined criterion to generate a validated point, wherein the drilling operation is performed based on the configuration and trajectory of the validated point.
Other aspects and advantages of the claimed subject matter will be apparent from the following description and the appended claims.

BRIEF DESCRIPTION OF DRAWINGS

Specific embodiments of the disclosed technology will now be described in detail with reference to the accompanying figures. Like elements in the various figures are denoted by like reference numerals for consistency.

FIG. 1 shows a system in accordance with one or more embodiments.

FIGS. 2A-2B show method flowcharts in accordance with one or more embodiments.

FIGS. 3A-3F show an example in accordance with one or more embodiments.

FIG. 4 shows a computing system in accordance with one or more embodiments.

DETAILED DESCRIPTION

In the following detailed description of embodiments of the disclosure, numerous specific details are set forth in order to provide a more thorough understanding of the disclosure. However, it will be apparent to one of ordinary skill in the art that the disclosure may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description.
Throughout the application, ordinal numbers (for example, first, second, third) may be used as an adjective for an element (that is, any noun in the application). The use of ordinal numbers is not to imply or create any particular ordering of the elements nor to limit any element to being only a single element unless expressly disclosed, such as using the terms “before”, “after”, “single”, and other such terminology. Rather, the use of ordinal numbers is to distinguish between the elements. By way of an example, a first element is distinct from a second element, and the first element may encompass more than one element and succeed (or precede) the second element in an ordering of elements.
In general, embodiments of the disclosure include a method and system for performing a drilling operation based on an optimized configuration and trajectory of a future production well. In one or more embodiments, a performance versus drilling-cost profile is used to facilitate rapid computation of an optimized well configuration and trajectory. In addition, the rapid computation of the optimized well configuration and trajectory may selectively be combined with reservoir-flow simulation when additional accuracy is needed.
FIG. 1 shows a schematic diagram in accordance with one or more embodiments. More specifically, FIG. 1 illustrates a well environment (100) that includes a hydrocarbon reservoir (“reservoir”) (102) located in a subsurface hydrocarbon-bearing formation (“formation”) (104) and a well system (106). In one or more embodiments of the disclosure, the reservoir (102) is a gas reservoir to produce condensate, referred to as a gas condensate reservoir. The hydrocarbon-bearing formation (104) may include a porous or fractured rock formation that resides underground, beneath the Earth's surface (“surface”) (108). In the case of the well system (106) being a hydrocarbon well, the reservoir (102) may include a portion of the hydrocarbon-bearing formation (104). The hydrocarbon-bearing formation (104) and the reservoir (102) may include different layers of rock (referred to as formation layers) having varying characteristics, such as varying degrees of permeability, porosity, capillary pressure, and resistivity. In the case of the well system (106) being operated as a production well, the well system (106) may facilitate the extraction of hydrocarbons (or “production”) from the reservoir (102). Although only one well system (106) and associated production well are shown in FIG. 1 , a large number of well systems and wells may exist in an oil/gas field, referred to as a field throughout this disclosure. The wells in the field may include production wells, injection wells, exploratory wells, wells under drilling, candidate wells to be extended or newly drilled, etc. The candidate wells include future extended or newly drilled production wells (i.e., candidate production wells), future extended or newly drilled injection wells (i.e., candidate injection wells), etc.
In some embodiments, the well system (106) includes a wellbore (120), a well sub-surface system (122), a well surface system (124), and a well control system (“control system”) (126). The control system (126) may control various operations of the well system (106), such as well production operations, well completion operations, well maintenance operations, and reservoir monitoring, assessment and development operations. In some embodiments, the control system (126) includes a computer system that is the same as or similar to that of the computer system (400) described below in FIG. 4 and the accompanying description.
The wellbore (120) may include a bored hole that extends from the surface (108) into a target zone of the hydrocarbon-bearing formation (104), such as the reservoir (102). Wellbores (120) in hydrocarbon production consist of one or more trajectories (main bore and laterals). An upper end of the wellbore (120), terminating at or near the surface (108), may be referred to as the “up-hole” end of the wellbore (120), and a lower end of the wellbore, terminating in the hydrocarbon-bearing formation (104), may be referred to as the “down-hole” end of the wellbore (120). The wellbore (120) may facilitate the circulation of drilling fluids during drilling operations, the flow of hydrocarbon production (“production”) (121) (e.g., oil and gas) from the reservoir (102) to the surface (108) during production operations, the injection of substances (e.g., water) into the hydrocarbon-bearing formation (104) or the reservoir (102) during injection operations, or the communication of monitoring devices (e.g., logging tools) into the hydrocarbon-bearing formation (104) or the reservoir (102) during monitoring operations (e.g., during in situ logging operations).
In some embodiments, during operation of the well system (106), the control system (126) collects and records wellhead data (140) for the well system (106). The wellhead data (140) may include, for example, a record of measurements of wellhead pressure (P_wh) (e.g., including flowing wellhead pressure), wellhead temperature (T_wh) (e.g., including flowing wellhead temperature), wellhead production rate (Q_wh) over some or all of the life of the well system (106), and water cut data. In some embodiments, the measurements are recorded in real-time, and are available for review or use within seconds, minutes or hours of the condition being sensed (e.g., the measurements are available within 1 hour of the condition being sensed). In such an embodiment, the wellhead data (140) may be referred to as “real-time” wellhead data (140). Real-time wellhead data (140) may enable an operator of the well system (106) to assess a relatively current state of the well system (106), and make real-time decisions regarding development of the well system (106) and the reservoir (102), such as on-demand adjustments in regulation of production flow from the well.
In some embodiments, the well sub-surface system (122) includes casing installed in the wellbore (120). For example, the wellbore (120) may have a cased portion and an uncased (or “open-hole”) portion. The cased portion may include a portion of the wellbore having casing (e.g., casing pipe and casing cement) disposed therein. The uncased portion may include a portion of the wellbore not having casing disposed therein. In some embodiments, the casing includes an annular casing that lines the wall of the wellbore (120) to define a central passage that provides a conduit for the transport of tools and substances through the wellbore (120). For example, the central passage may provide a conduit for lowering logging tools into the wellbore (120), a conduit for the flow of production (121) (e.g., oil and gas) from the reservoir (102) to the surface (108), or a conduit for the flow of injection substances (e.g., water) from the surface (108) into the hydrocarbon-bearing formation (104). In some embodiments, the well sub-surface system (122) includes production tubing installed in the wellbore (120). The production tubing may provide a conduit for the transport of tools and substances through the wellbore (120). The production tubing may, for example, be disposed inside casing. In such an embodiment, the production tubing may provide a conduit for some or all of the production (121) (e.g., oil and gas) passing through the wellbore (120) and the casing.
In some embodiments, the well surface system (124) includes a wellhead (130). The wellhead (130) may include a rigid structure installed at the “up-hole” end of the wellbore (120), at or near where the wellbore (120) terminates at the Earth's surface (108). The wellhead (130) may include structures for supporting (or “hanging”) casing and production tubing extending into the wellbore (120). Production (121) may flow through the wellhead (130), after exiting the wellbore (120) and the well sub-surface system (122), including, for example, the casing and the production tubing. In some embodiments, the well surface system (124) includes flow regulating devices that are operable to control the flow of substances into and out of the wellbore (120). For example, the well surface system (124) may include one or more production valves (132) that are operable to control the flow of production (121). For example, a production valve (132) may be fully opened to enable unrestricted flow of production (121) from the wellbore (120), the production valve (132) may be partially opened to partially restrict (or “throttle”) the flow of production (121) from the wellbore (120), and production valve (132) may be fully closed to fully restrict (or “block”) the flow of production (121) from the wellbore (120), and through the well surface system (124).
Keeping with FIG. 1 , in some embodiments, the well surface system (124) includes a surface sensing system (134). The surface sensing system (134) may include sensors for sensing characteristics of substances, including production (121), passing through or otherwise located in the well surface system (124). The characteristics may include, for example, pressure, temperature and flow rate of production (121) flowing through the wellhead (130), or other conduits of the well surface system (124), after exiting the wellbore (120).
In some embodiments, the surface sensing system (134) includes a surface pressure sensor (136) operable to sense the pressure of production (121) flowing through the well surface system (124), after it exits the wellbore (120). The surface pressure sensor (136) may include, for example, a wellhead pressure sensor that senses a pressure of production (121) flowing through or otherwise located in the wellhead (130), referred to as wellhead pressure (P_wh). In some embodiments, the surface sensing system (134) includes a surface temperature sensor (138) operable to sense the temperature of production (121) flowing through the well surface system (124), after it exits the wellbore (120). The surface temperature sensor (138) may include, for example, a wellhead temperature sensor that senses a temperature of production (121) flowing through or otherwise located in the wellhead (130), referred to as “wellhead temperature” (T_wh). In some embodiments, the surface sensing system (134) includes a flow rate sensor (139) operable to sense the flow rate of production (121) flowing through the well surface system (124), after it exits the wellbore (120). The flow rate sensor (139) may include hardware that senses a flow rate of production (121) (Q_wh) passing through the wellhead (130).
In some embodiments, the well system (106) includes a well configuration and trajectory analyzer (160). For example, the well configuration and trajectory analyzer (160) may include hardware and/or software with functionality for generating one or more optimized well configuration(s) and trajectory(ies) and/or performing one or more reservoir simulations. For example, the well configuration and trajectory analyzer (160) may accelerate computational optimization of configuration and trajectory for multiple wells in a hydrocarbon field. A well in hydrocarbon production may consist of one or more trajectories, referred to as the main bore and lateral(s). Well configuration as used herein refers to the number of laterals.
In one or more embodiments, the well configuration and trajectory analyzer (160) may use an optimization cost function based on a general economic metric, for example, net present value, and includes the revenue associated with the hydrocarbon produced within a given interval, production expenses such as those corresponding to water-hydrocarbon separation and also the well drilling cost. In one or more embodiments, the well configuration and trajectory analyzer (160) is configured to use a performance versus drilling-cost profile (referred to as “profile” throughout this disclosure) to accelerate the optimization. This acceleration is based on utilizing the profile to rank the wells in the field, then selecting a subset of the top-ranked wells and, for these wells, determining a small number of configurations and trajectories to be simulated. From each well profile, a small number of points are validated through simulation within an optimization framework. The profiles are computed quickly because they are obtained through a number of metrics that do not require reservoir-flow simulation. The information that is summarized in each profile may be used to support rapid decision-making for drilling operations based on the optimized well configuration and trajectory.
In some embodiments, the well configuration and trajectory analyzer (160) may further include a reservoir simulator for performing simulation, such as reservoir-flow simulation that considers, for example, fluid dynamics as a function of time and interaction between wells. In a typical reservoir simulation, a mathematical model of the reservoir includes a set of partial differential equations representing reservoir and well flows that are solved numerically. Numerical solution involves time and space/domain discretization replacing differential equations with difference equations. Time discretization refers to division of time into a sequence of time steps. In each time step, after discretization is solved iteratively, a non-linear system is linearized using Newton method, which may take several Newton iterations to converge. Space/domain discretization, also called grid generation, refers to division of the reservoir domain into a reservoir grid of small grid blocks. A grid is a tessellation of a set of contiguous polygonal (2D) or polyhedral (3D) objects referred to as grid blocks/cells/elements/control volumes. The grid generation is a process of discretization of the reservoir using both structured and more complex unstructured grid blocks to accurately represent the geometry of the reservoir.
While the well configuration and trajectory analyzer (160) is shown at a well site, embodiments are contemplated where reservoir simulators are located away from well sites. In some embodiments, the well configuration and trajectory analyzer (160) may include a computer system that is similar to the computer system (400) described below with regard to FIG. 4 and the accompanying description.
FIGS. 2A-2B show flowcharts in accordance with one or more embodiments disclosed herein. One or more of the steps in FIGS. 2A-2B may be performed by the components of the well environment (100) and the well configuration and trajectory analyzer (160), discussed above in reference to FIG. 1 . In one or more embodiments, one or more of the steps shown in FIGS. 2A-2B may be omitted, repeated, and/or performed in a different order than the order shown in FIGS. 2A-2B. Accordingly, the scope of the disclosure should not be considered limited to the specific arrangement of steps shown in FIGS. 2A-2B.
Turning to FIG. 2A, FIG. 2A shows a process flowchart for generating an optimized well configuration and trajectory of candidate wells to facilitate drilling operations in a field. The output of the optimization process includes the well configurations and trajectories for the candidate wells. In one or more embodiments, the configuration refers to the number of laterals for each candidate well. The configurations and trajectories are optimized with respect to a pre-determined cost function, such as the net present value associated with the hydrocarbon production and drilling of the candidate wells according to the selected well configurations and trajectories. For example, the hydrocarbon production may be based on waterflooding. Alternatively, the optimization process may also be applied to other hydrocarbon recovery mechanisms, known to those with ordinary skill in the art.
In one or more embodiments, the configurations and trajectories of multiple candidate wells are optimized as a multi-objective optimization that generates the Pareto optimal solution. The dominant solution and optimal value in the multi-objective optimization are usually achieved when one objective function cannot increase without reducing the other objective function(s). This condition is referred to as Pareto optimality. The set of optimal solutions in the multi-objective optimization is referred to as the Pareto optimal solution, or the Pareto front. Throughout this disclosure, the terms “search” or “optimization” refer to searching for or otherwise generating the Pareto optimal solution.
Initially in Step 200, the information of the field is input into a well configuration and trajectory analyzer as the specification for the optimization task. The field information includes (i) a baseline development plan of the field with N_prewells already drilled and N candidate wells to be drilled or that are drilled but their trajectories have to be extended to add one or more branches (laterals), (ii) the target-entries (TEs) defined as a well entry point where the well enters the hydrocarbon reservoir, and any existing trajectories for these N candidate wells, (iii) the number of candidate production wells (denoted as N_P) and the number of candidate injection wells (denoted as N_I) where N=N_I+N_P, (iv) the reservoir model including the discretization grid of the reservoir, and rock and fluid properties assigned to grid blocks (referred to as model data), and (v) optimization parameters and constraints.
Each well to be analyzed by the well configuration and trajectory analyzer may be uniquely identified by the corresponding TE. Rock and fluid properties may include and/or relate to distributions of rock permeability and porosity, hydrocarbon and water saturation, reservoir pressure, net-to-gross ratio, facies and relative-permeability curves, etc. Examples of optimization parameters and constraints include the oil sale price, water handling and injection costs, discount factor, minimum well bottom-hole pressure, maximum well water-cut and liquid rate, field oil production-rate target, maximum number of well laterals, and minimum inter-well distance.
In Step 201, a performance versus drilling-cost profile is computed or otherwise determined for each well. The well-performance versus drilling-cost profile consists of a collection of value pairs of the performance and the drilling cost. Using the performance versus drilling-cost profile, the well-drilling cost and a well-performance metric are used as optimization cost functions, i.e., the multi-objectives for generating the Pareto optimal solution. The cost functions (i.e., well-drilling cost and the well-performance metric) are fast-to-compute so that a relatively accurate solution of the optimization problem may be obtained rapidly. The well drilling cost can be determined through direct interpolation of existing tables and historical data of already drilled wells in the field. The well-performance metric is determined by estimating well performance in a computationally efficient manner. The precision of the optimal solution obtained with this approximate model may be selectively increased later by performing a number of selected simulations based on available computing resources. In other words, a higher number of simulations are performed if more computing resources are available, while a smaller number of simulations (or no simulation) are performed if less computing resources are available. An example of the well-performance metric for a production well based on waterflooding mechanism is the Opportunity Index (OI):
$\begin{matrix} OI = \sqrt{RQI \cdot MOI}, & Eq . (1) \end{matrix}$
where the Reservoir Quality Index (RQI) is given by
$\begin{matrix} RQI = 0.0314 \sqrt{\frac{k \cdot k_{row}}{ϕ}}, & Eq . (2) \end{matrix}$
where k denotes the absolute rock permeability, k_rowdenotes the relative permeability of oil in the presence of water, and ϕ denotes the rock porosity, and the Mobile Oil Index (MOI) is given by
$\begin{matrix} MOI = D_{w} \cdot NTG \cdot ϕ \cdot (S_{o} - S_{orw}), & Eq . (3) \end{matrix}$
where D_wdenotes the grid dimension in the direction of the well segment in that grid block, NTG denotes the net-to-gross ratio, S_odenotes the oil saturation, and S_odenotes the residual oil saturation. The variable magnitudes in Eqs. (1)-(3) are computed for any grid block in the simulation grid and are subsequently aggregated for applicable grid blocks, e.g., for each grid block that includes the trajectory of the main bore and possible laterals. Alternatively, the aggregation may also include neighboring grid blocks surrounding the grid blocks intersected by the well trajectory. The OI in Eqs. (1)-(3) may be modified as the cost function for injection wells, where variable magnitudes related to oil are substituted by those corresponding to water (i.e., water relative permeability and water saturation). Well-performance metrics other than OI may also be used as the cost function, such as productivity and injectivity indexes based on Peaceman's well index. Metrics related to reservoir pressure and distance to injection and production wells may also be incorporated into the well-performance metric.
As indicated above, the computation of the performance versus drilling-cost profile relates to Pareto dominance from multi-objective optimization. During the multi-objective optimization, a potential configuration and corresponding trajectory/trajectories for the production well is referred to as a point. A first point is said to dominate a second point when all the optimization cost-function values associated with the first point are not worse, respectively, than the optimization cost-function values that correspond to the second point, and when at least one optimization cost-function value associated with the first point is strictly better than the respective optimization cost function value from the second point. A well-performance versus drilling-cost profile consists of all the dominating points found during the multi-objective optimization for a given well. In one or more embodiments, the cost function is based on performance and drilling cost of the potential point, and the dominating point is a value pair of the performance and the drilling cost.
FIG. 3A shows a plot (301) of the well-performance metric and drilling cost for a production well with a given TE within a region of a field that includes more than 400,000 dots (each dot corresponds to a value pair). The well-performance metric and well-drilling cost have been scaled (i.e., normalized) to be within the unit interval [0, 1]. FIG. 3B shows the well-performance versus drilling-cost profile (302) consisting of all the dominating points of the more than 400,000 dots in the plot (301) depicted in FIG. 3A. Further details of Step 201, i.e., computing the performance versus drilling-cost profile for a candidate well is described in reference to FIG. 2B below.
In Step 202, the performance versus drilling-cost profiles for all the wells in the field are output for decision support. The performance versus drilling-cost profiles may be directly visualized or post-processed to generate various graphical outputs, such as maps of well-drilling directions and numbers of laterals for all candidate wells in the field.
In Step 203, the candidate wells are ranked according to respective performance versus drilling-cost profiles. The ranking may be based on either one of the well-performance metric and the drilling cost, or based on a combination of the well-performance metric and the drilling cost of a point or set of points. For example, the ranking may be based on the highest value of well-performance metric in the scenario when hydrocarbon production is significantly more important than the economic impact of well-drilling cost. The ranking classifies candidate wells and, more generally, regions where new wells are to be drilled or where already drilled wells are to be extended or be added with branches (laterals).
In Step 204, a subset of top ranked NW wells are selected, according to the ranking, from the N wells for optimization. The subscript “w” refers to wells. The ratio between the numbers of injection and production wells within the NW wells may be, for example, approximately equal to the ratio between N_Iand N_P.
In Step 205, the specifications for computing resources are used as a parameter for Step 204 to select the N_wwells. The specifications for computing resources may be expressed in terms of wall-clock time and translated into a budget (e.g., number) of reservoir-flow simulations to be performed. Here, the budget may be assumed to equal to N_ssimulations. In other words, the number of simulations performed in Step 205 is limited to no more than N_ssimulations. For each of the N_wwells with a corresponding performance versus drilling-cost profile, at most a number of points N_proare chosen from the corresponding profile and validated through simulation (Step 209) within an optimization framework where each of these points N_prorepresents a choice for configuration and trajectory.
Multiple criteria can be used to choose these points. For example, if the number of points in the profile is smaller than or equal to N_pro, then all points in the profile are selected. If the number of points in the profile is larger than N_pro, then these points can be chosen as those which associated OI is closer to a given percentile of all the OIs. For example, if N_prois equal to 3 and the profile has more than three points, selection can be based on proximity to the 25th, 50th and 75th percentiles. Additional selection rules may be needed in case one point in the profile is the closest to more than one percentile. Alternatively, the N_propoints can be chosen by means of clustering algorithms, or, if the maximum number of laterals allowed for the well to optimize is equal to N_pro−1, then each point can be selected from the subset of points that yield wells with no laterals, one lateral, two laterals, etc. In each of these subsets, the point chosen may be the closest to the centroid of the subset, that is, the mean of the points in the subset.
An example of selecting points N_prois shown in FIG. 3C where N_pro=3 points (303 a, 303 b, 303 c) are selected from the performance versus drilling-cost profile (302) depicted in FIG. 3B. The corresponding well configurations and trajectories (313 a, 313 b, 313 c) of the points (303 a, 303 b, 303 c) are inserted in FIG. 3C for illustration. This particular selection of N_pro=3 points (303 a, 303 b, 303 c) aims at even sampling of the well-performance versus drilling-cost profile (302), which results in corresponding candidate wells with different drilling complexity and cost.
Selection is based on proximity to three percentiles of the distribution of all the OI values in the profile. The percentiles are tuned so that the sampling performed was relatively even. It may be expected that other candidate wells, especially those located nearby, have similar distributions of points in the profile. If that is not the case, the percentiles can be modified or other selection strategies, e.g., based on clustering, may be considered.
In an example scenario, N_prois the same for all N_wwells while in other scenarios N_promay vary across the N_wwells, e.g., proportional to the respective average well-performance metric. N_wtimes N_prois less than or equal to N_swhen N_prois the same for all wells. A relatively small number of additional simulations may be performed to optionally improve the Pareto optimal solution. N_optdenotes the number of simulations and may correspond, for example, to iterations of a certain optimization procedure. Consequently, N_s−N_opt, the number of simulations associated with the use of the well-performance versus drilling-cost profile may typically be a large fraction of N. For example, in the optimization of a given field with N_I=40 and N_P=160 injection and production wells, if N_sis 100 reservoir-flow simulations, N_optmay be 25 and N_pro=3 points may be selected from the well-performance versus drilling-cost profiles of the top-ranked N=25 wells (out of which 5 and 20 are injection and production wells, respectively).
In Step 206, a determination is made as to whether to refine the selected profile (e.g., well-performance versus drilling-cost profile (302)). In some embodiments, the determination is based on a user selection input. In other embodiments, the determination is based on the selected profiles having a relatively low number of selected points N_pro. If the determination is positive, i.e., to refine the selected profile, the method proceeds to Step 207. If the determination is negative, i.e., not to refine the selected profile, the method proceeds to Step 209.
In Step 207, the performance versus drilling-cost profiles that have been determined in Step 201 are refined. The refinement process is essentially the same as, and incremental to, Steps 200-201.
In Step 208, a user selection is received as to whether to refine the search based on other information than the well profiles, for example, through one or more iterations of an optimization procedure. If the user selects to refine the search, the method proceeds to Step 212. If the user selects not to refine the search, the method proceeds to Step 209.
In Step 209, simulations are performed to validate configurations and trajectories for the wells selected in Step 204. The configurations and trajectories of the selected candidate well are validated if the simulated values of the cost function satisfies a pre-determined criterion. For example, the cost function may correspond to an economic metric such as net present value that takes into consideration future injection and production fluid rates as a function of time. The pre-determined criterion may correspond to a target improvement with respect to an existing baseline or to previously validated candidate wells.
Note that, for the sake of efficiency, multiple simulations may be performed concurrently within a distributed-computing framework. Many optimization constraints may be directly incorporated in the simulation. Examples of these constraints are minimum well bottom-hole pressure, maximum well water-cut and liquid rate and field oil production-rate target. Other constraints may be observed, for example, by discarding infeasible solutions, introduction of penalties or through general-constraint-handling techniques such as the filter method.
The effects associated with the interaction between all the wells, including the optimized candidate wells, are captured in the final solution consisting of new configurations and trajectories of the N candidate wells. This may be obtained, for example, if after running N_prosimulations for a given well, the best configuration and trajectory in terms of optimization cost function for the given well is selected and set for the remaining simulations of other wells in the overall optimization. Alternatively, if the interaction effects between the optimized wells may be neglected, for example, if these wells are located sufficiently far away from one another, the configuration and trajectory to change with respect to the baseline solution per simulation would be only the configuration and trajectory related to each optimized well. In these circumstances, all simulations may be performed concurrently if distributed-computing resources are available.
In Step 210, a determination is made as to whether to end the simulations prior to completing all N_optsimulations. The determination is based on a termination criterion that is specified in Step 211 with all optimization parameters. If the determination is to end the simulations, the method proceeds to Step 213. If the determination is not to end the simulations, the method returns to Step 206.
In Step 211, optimization parameters are specified together with the termination criterion. An example termination criterion is a relative improvement in cost-function value with respect to a baseline. Early termination may be convenient in order to free computing resources for tasks. The simulations associated with the profiles may be evaluated in different batches, for example, one point from each profile is evaluated at a time. As used herein, a batch is a set of simulations to be run. A batch or a subset of a batch can be run concurrently in a distributed-computing environment.
In Step 212, candidate points outside of the profile (e.g., profile (302) depicted in FIG. 3B) are selected to refine the optimal search.
If the optimal search is refined as determined in Step 208, new points may be generated in Step 212 using other information separate from the well profiles. For example, new points may be generated through one or more iterations of an optimization procedure. The application of an optimization algorithm requires the mathematical formulation of the optimization problem, in particular, the definition of the search space. A possible definition of this space for rectilinear well trajectories may rely on an integer-valued variable that specifies the number of laterals of the well and on multiple real-valued variables that define the main bore and laterals. More general trajectories may be represented by additional variables. Example constraints for these variables may include preventing trajectories from being too close to one another and/or avoiding angles between trajectories that are difficult to drill. In the optimization of well configuration and trajectory, the high heterogeneity of reservoir rock properties contributes to the presence of multiple local solutions. This type of optimization spaces may be approached by means of techniques such as genetic algorithms and particle swarm optimization that incorporate stochastic components in the search. Previous simulations may be leveraged to construct fast-to-evaluate surrogates for the cost function and constraints and to efficiently compute new points that may be validated through simulation.
In Step 213, the optimized well configurations and trajectories are provided as output to the user for inspection, visualization, and further analysis.
In Step 214, drilling operations are performed based on the optimized well configurations and trajectories. For example, one or more production wells and/or injection wells may be extended by continuing the drilling of the main bore or branched out by drilling one or more lateral branches based on the optimized well configurations and trajectories.
Turning to FIG. 2B, FIG. 2B shows a process flowchart for computing the performance versus drilling-cost profile for a candidate well, corresponding to Step 201 depicted in FIG. 2A above.
Initially, in Step 220, a search region is obtained for each new candidate well. A new candidate well is a candidate well where the performance versus drilling-cost profile has not been computed and is to be newly computed. The search region is a three-dimensional region around the corresponding candidate well to search for the optimal well configuration and trajectory. The search region is determined to satisfy, if possible, a pre-determined inter-well distance constraint. For each new candidate well, a sufficiently small initial region exists such that all trajectories in that region that start from the well TE or extend from existing trajectories for the candidate well do not intersect trajectories from other existing wells. The initial region may be obtained from a previous analysis to determine which wells whose configuration and trajectory are optimized. The initial region is modified to comply with the inter-well distance constraint. The modification may be based on evaluating multiple boxes that include the initial region and selecting one of the boxes (e.g., the box with the smallest volume) that satisfy the inter-well distance constraint. In the cases where the pre-determined inter-well distance constraint cannot be satisfied in a particular search region, an alternative minimum and feasible inter-well distance is determined for the optimal well configuration and trajectory search in that particular region. The computational cost associated with finding each search region in this manner is, in general, negligible when compared with the cost of a reservoir-flow simulation.
In Step 221, the trajectories of existing wells (if any) within the search region are obtained as additional data to determine the search region around each well.
In Step 222, simulation grid and model data related to the new candidate wells are obtained as additional data to supplement the computation of the search region around each well. Thus, both Steps 221 and 222 are inputs to Step 220 and are used to obtain this search region.
In Step 223, the iterative index of the search region is specified. Based on iteratively incremented indices, each search region is iteratively analyzed in Steps 224-231 for the corresponding profile computation. In this context, the search region being analyzed is referred to as the search region i, where i denotes the iterative index. As noted above, each candidate well is uniquely identified by the corresponding TE, each search region i thus includes the unique TE of the corresponding candidate well.
In Step 224, multiple well configurations and trajectories are generated for the search region i. If there is no existing well in the search region i, the trajectories all start from the TE of the candidate well in the search region i. Otherwise, the trajectories are set to be consistent with the existing well. Next, rectilinear trajectories are generated for the main bore and laterals. A rectilinear trajectory is a trajectory that is formed from one straight line and suffices in many practical situations. Given the TE, a well with rectilinear trajectories and n_latlaterals may be parameterized (i.e., specified) through coordinates of the end of the main bore, and start and end of each lateral. If the laterals are drilled from the main bore, which is a common practice, the respective starting points may be specified as scalar quantities. For example, the starting point and end point of the main bore may be parameterized by 0 and 1, respectively. Thus, a well with rectilinear trajectory that starts from the TE and with n_latlaterals drilled from the main bore may be described using 3+4 n_latparameters. In particular, 3 parameters are coordinates of the end point of the main bore. In addition, each lateral requires 4 parameters, which includes one parameterized parameter between 0 and 1 to define the starting point (e.g., 0 being the starting point of the main bore and 1 being the end point of the main bore) and 3 parameters to specify the end point of each lateral, which are coordinates of the end point of each lateral.
In case the optimization is for extending an existing trajectory starting from a given TE, a subset of these 3+4 n_latparameters is already set and may be ignored. For laterals that are drilled from other laterals, the starting point of each lateral may be parameterized by means of a scalar quantity. For example, for the existing well having two laterals, the starting point of the first lateral may be defined using a quantity between 0 and 1, and the starting point of the second lateral, which may be drilled from the first lateral, may be defined using a quantity between 0 and 1 if drilled from the main bore and using a quantity between 1 and 2 if drilled from the first lateral.
In order to generate configurations and trajectories, the number and start/end of laterals are subject to a number of constraints. The constraints may ensure, for example, that the number of laterals is smaller than a previously determined maximum number of laterals, that laterals and main bore do not intersect, that two laterals do not start from excessively close locations and that the angles between trajectories are not smaller or greater than pre-determined drilling tolerances.
FIG. 3D-3F show three wells (304 a, 304 b, 304 c) starting from the same TE in the search region i (304), with rectilinear trajectories and two laterals drilled from the main bore. No existing wells have been drilled previously from that TE.
Other general types of non-rectilinear trajectories may be parameterized for analysis by introducing additional parameters and constraints. Note that many reservoir-flow simulators represent well trajectories as a sequence of segments along the three axes of a Cartesian coordinate system. Trajectories may be specified in a compatible format with the simulator used.
In Step 225, reservoir and fluid properties for the search region i are obtained from a reservoir model or other database as input to Step 226 for computing the performance index.
In Step 226, components of the well profile are computed for each generated trajectory. In particular, the well performance index is computed, for example, based on Eqs. (1)-(3).
In Step 227, a drilling-cost model is obtained as input to the Step 228 for computing the drilling cost. The drilling-cost cost model is used to determine the cost of drilling a new well and drilling an extension of an existing well. The drilling-cost cost model may be based on an empirical formula to determine the drilling-cost as a function of well depth, number of laterals to be drilled and total reservoir contact per main bore and lateral.
In Step 228, the drilling cost is determined based on the drilling-cost model as an additional component of the well profile.
In Step 229, the profile for each generated trajectory in the search region i is updated based on Pareto dominance.
In Step 230, a determination is made as to whether the search for the optimal profile is to end. If the determination is positive, i.e., the search for the optimal profile is to end, the method proceeds to Step 231. If the determination is negative, i.e., the search for the optimal profile is to continue, the method returns to Step 224.
The determination in Step 230 allows for additional trajectories to be added for the Pareto optimal solution analysis to refine the profile as a well-profile update. For example, the additional trajectories may be generated randomly until termination criteria are satisfied. The well-profile update may be used as a base of the Step 207 depicted in FIG. 2A above.
In Step 231, a determination is made as to whether all search regions have been analyzed. If the determination is positive, i.e., all search regions have been analyzed, the method proceeds to Step 232. If the determination is negative, i.e., one or more search regions remain to be analyzed, the method returns to Step 223.
In Step 232, well profiles of all search regions are output for presenting to a user as in the Step 202 depicted in FIG. 2A above. Further, well profiles of all search regions are output for ranking wells as in the Step 203 depicted in FIG. 2A above.
The well configuration and trajectory optimization method is tested on five wells selected from different regions of a field. All five wells have to be drilled from the previously specified TEs and no previous trajectory existed in each case. The five wells are production wells based on waterflooding. The illustrative results are based on comparing simulated oil production of the optimized configurations and trajectories (referred to as the optimized wells) through a simulation model of the field with respect to the wells actually drilled (referred to as the baseline wells). During the first five years after drilling and according to the simulation, the optimized wells produce around 11% more oil than the baseline wells. The increase in oil produced is approximately 12% for the 20 years after drilling. For both production periods, the optimized wells also produce more water. However, the cost associated with separating the additional water and with the actual drilling of the optimized wells is offset in most economic scenarios, in particular, in those where net present value is mainly driven by the volume of oil and the corresponding sale price. Note that only one reservoir-flow simulation was selected per optimized well to validate the obtained trajectory and configuration in each case.
The computing performance advantage may be especially significant in scenarios that typically demand significant computational resources, such as optimization under geological uncertainty. With geological uncertainty, multiple models of the hydrocarbon field are available and predictions rely on running a number of these models each time. Accordingly, the computational cost of an optimization process that incorporates geological uncertainty becomes much higher than when a single model is considered. When geological uncertainty is incorporated in the optimization, the number of simulations may be reduced, or adjusted based on available computing resources, using the well configuration and trajectory optimization method described above.
Embodiments may be implemented on a computer system. FIG. 4 is a block diagram of a computer system (400) used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures as described in the instant disclosure, according to an implementation. The illustrated computer (402) is intended to encompass any computing device such as a high performance computing (HPC) device, a server, desktop computer, laptop/notebook computer, wireless data port, smart phone, personal data assistant (PDA), tablet computing device, one or more processors within these devices, or any other suitable processing device, including both physical or virtual instances (or both) of the computing device. Additionally, the computer (402) may include a computer that includes an input device, such as a keypad, keyboard, touch screen, or other device that may accept user information, and an output device that conveys information associated with the operation of the computer (402), including digital data, visual, or audio information (or a combination of information), or a GUI.
The computer (402) may serve in a role as a client, network component, a server, a database or other persistency, or any other component (or a combination of roles) of a computer system for performing the subject matter described in the instant disclosure. The illustrated computer (402) is communicably coupled with a network (430). In some implementations, one or more components of the computer (402) may be configured to operate within environments, including cloud-computing-based, local, global, or other environment (or a combination of environments).
At a high level, the computer (402) is an electronic computing device operable to receive, transmit, process, store, or manage data and information associated with the described subject matter. According to some implementations, the computer (402) may also include or be communicably coupled with an application server, e-mail server, web server, caching server, streaming data server, business intelligence (BI) server, or other server (or a combination of servers).
The computer (402) may receive requests over network (430) from a client application (for example, executing on another computer (402)) and responding to the received requests by processing the said requests in an appropriate software application. In addition, requests may also be sent to the computer (402) from internal users (for example, from a command console or by other appropriate access method), external or third-parties, other automated applications, as well as any other appropriate entities, individuals, systems, or computers.
Each of the components of the computer (402) may communicate using a system bus (403). In some implementations, any or all of the components of the computer (402), both hardware or software (or a combination of hardware and software), may interface with each other or the interface (404) (or a combination of both) over the system bus (403) using an application programming interface (API) (412) or a service layer (413) (or a combination of the API (412) and service layer (413). The API (412) may include specifications for routines, data structures, and object classes. The API (412) may be either computer-language independent or dependent and refer to a complete interface, a single function, or even a set of APIs. The service layer (413) provides software services to the computer (402) or other components (whether or not illustrated) that are communicably coupled to the computer (402). The functionality of the computer (402) may be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer (413), provide reusable, defined business functionalities through a defined interface. For example, the interface may be software written in JAVA, C++, or other suitable language providing data in extensible markup language (XML) format or other suitable format. While illustrated as an integrated component of the computer (402), alternative implementations may illustrate the API (412) or the service layer (413) as stand-alone components in relation to other components of the computer (402) or other components (whether or not illustrated) that are communicably coupled to the computer (402). Moreover, any or all parts of the API (412) or the service layer (413) may be implemented as child or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of this disclosure.
The computer (402) includes an interface (404). Although illustrated as a single interface (404) in FIG. 4 , two or more interfaces (404) may be used according to particular needs, desires, or particular implementations of the computer (402). The interface (404) is used by the computer (402) for communicating with other systems in a distributed environment that are connected to the network (430). Generally, the interface (404) includes logic encoded in software or hardware (or a combination of software and hardware) and operable to communicate with the network (430). More specifically, the interface (404) may include software supporting one or more communication protocols associated with communications such that the network (430) or interface's hardware is operable to communicate physical signals within and outside of the illustrated computer (402).
The computer (402) includes at least one computer processor (405). Although illustrated as a single computer processor (405) in FIG. 4 , two or more processors may be used according to particular needs, desires, or particular implementations of the computer (402). Generally, the computer processor (405) executes instructions and manipulates data to perform the operations of the computer (402) and any algorithms, methods, functions, processes, flows, and procedures as described in the instant disclosure.
The computer (402) also includes a memory (406) that holds data for the computer (402) or other components (or a combination of both) that may be connected to the network (430). For example, memory (406) may be a database storing data consistent with this disclosure. Although illustrated as a single memory (406) in FIG. 4 , two or more memories may be used according to particular needs, desires, or particular implementations of the computer (402) and the described functionality. While memory (406) is illustrated as an integral component of the computer (402), in alternative implementations, memory (406) may be external to the computer (402).
The application (407) is an algorithmic software engine providing functionality according to particular needs, desires, or particular implementations of the computer (402), particularly with respect to functionality described in this disclosure. For example, application (407) may serve as one or more components, modules, applications, etc. Further, although illustrated as a single application (407), the application (407) may be implemented as multiple applications (407) on the computer (402). In addition, although illustrated as integral to the computer (402), in alternative implementations, the application (407) may be external to the computer (402).
There may be any number of computers (402) associated with, or external to, a computer system containing computer (402), each computer (402) communicating over network (430). Further, the term “client,” “user,” and other appropriate terminology may be used interchangeably as appropriate without departing from the scope of this disclosure. Moreover, this disclosure contemplates that many users may use one computer (402), or that one user may use multiple computers (402).
In some embodiments, the computer (402) is implemented as part of a cloud computing system. For example, a cloud computing system may include one or more remote servers along with various other cloud components, such as cloud storage units and edge servers. In particular, a cloud computing system may perform one or more computing operations without direct active management by a user device or local computer system. As such, a cloud computing system may have different functions distributed over multiple locations from a central server, which may be performed using one or more Internet connections. More specifically, cloud computing system may operate according to one or more service models, such as infrastructure as a service (IaaS), platform as a service (PaaS), software as a service (SaaS), mobile “backend” as a service (MBaaS), serverless computing, artificial intelligence (AI) as a service (AIaaS), and/or function as a service (FaaS).
Embodiments include the following advantages: (i) increase in hydrocarbon well productivity due to more accurate optimization of well configurations and trajectories, (ii) reduction of computational resources (e.g., computing time and infrastructure, simulation software licenses, etc.) for optimization of hydrocarbon-production well configuration and trajectory, and (iii) fast generation of inputs in decision-making processes for configuration and trajectory of hydrocarbon-production wells.
Although only a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the example embodiments without materially departing from this invention. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims.

Claims

What is claimed:

1. A method to perform a drilling operation in a field, comprising:

computing a plurality of profiles of a plurality of candidate wells in the field, wherein each profile comprises a plurality of points associated with a corresponding well, each point corresponding to a configuration and trajectory of said corresponding well and comprises a value pair of performance and drilling cost of said corresponding well;

generating, based on the plurality of profiles, a ranking of the plurality of candidate wells;

selecting, from the plurality of candidate wells and according to the ranking, a plurality of selected candidate wells;

selecting, from the plurality of points associated with each selected candidate well, a number of selected points;

performing, for each of the number of selected points, reservoir simulation to generate a plurality of simulation results;

comparing the plurality of simulation results to a pre-determined criterion to generate a validated point; and

performing, based on the configuration and trajectory of the validated point, the drilling operation.

2. The method of claim 1,

wherein selecting the plurality of selected candidate wells is based on available computing resources for performing the reservoir simulation.

3. The method of claim 1, wherein computing the plurality of profiles of the plurality of candidate wells comprises:

obtaining a search region surrounding a well entry point of a candidate well of the plurality of candidate wells;

generating, within the search region, a plurality of potential configurations and trajectories;

computing, for each of the plurality of potential configurations and trajectories, the value pair of performance and drilling cost;

selecting, from the plurality of value pairs of performance and drilling cost, a plurality of dominating points based on Pareto dominance; and

wherein the profile of the candidate well comprises the value pair of performance and drilling cost for each of the plurality of dominating points.

4. The method of claim 3,

wherein generating the plurality of potential configurations and trajectories is based on a pre-determined inter-well constraint.

5. The method of claim 3, further comprising:

obtaining rock and fluid properties of the field, wherein computing the performance in the value pair is based on the reservoir and fluid properties; and

obtaining a drilling-cost model of the field, wherein computing the drilling cost in the value pair is based on the drilling-cost model.

6. The method of claim 1,

wherein each of the plurality of candidate wells is specified by a well entry point to a reservoir in the field,

wherein the configuration comprises a main bore and a number of laterals.

7. The method of claim 6,

wherein each of the main bore and laterals comprises a rectilinear well trajectory.

8. A well configuration and trajectory analyzer to facilitate a drilling operation in a field, comprising:

a computer processor; and

memory storing instructions, when executed by the computer processor comprising functionality for:

performing, for each of the number of selected points, reservoir simulation to generate a plurality of simulation results; and

comparing the plurality of simulation results to a pre-determined criterion to generate a validated point,

wherein the drilling operation is performed based on the configuration and trajectory of the validated point.

9. The well configuration and trajectory analyzer of claim 8,

10. The well configuration and trajectory analyzer of claim 8, wherein computing the plurality of profiles of the plurality of candidate wells comprises:

11. The well configuration and trajectory analyzer of claim 10,

12. The well configuration and trajectory analyzer of claim 10, the instructions, when executed by the computer processor further comprising functionality for:

13. The well configuration and trajectory analyzer of claim 8,

wherein the configuration comprises a main bore and a number of laterals.

14. The well configuration and trajectory analyzer of claim 13,

15. A system comprising:

a wellsite in a field for performing a drilling operation; and

a well configuration and trajectory analyzer comprising a computer processor and memory storing instructions, when executed by the computer processor comprising functionality for:

16. The system of claim 15,

17. The system of claim 15, wherein computing the plurality of profiles of the plurality of candidate wells comprises:

18. The system of claim 17,

19. The system of claim 17, the instructions, when executed by the computer processor further comprising functionality for:

20. The system of claim 15,

wherein the configuration comprises a main bore and a number of laterals.