CN114021717A - Oil and gas reservoir productivity prediction method and device - Google Patents
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Abstract
The embodiment of the invention discloses a method and a device for predicting the productivity of an oil and gas reservoir, wherein the method comprises the following steps: preprocessing logging data and reservoir productivity data to construct a standardized sample set; constructing and training a constraint neural network according to the sample set, and adding neurons in a constraint layer of the constraint neural network as constraint elements to constrain calculation of the constraint neural network; the input of the constraint element is calculated by a rock mechanics seepage model; and acquiring logging data of the reservoir to be predicted, and inputting the logging data into the constraint neural network to obtain the reservoir capacity under unit pressure difference. According to the invention, the constraint element is added into the constraint neural network, the constraint neural network under the constraint of the rock mechanics seepage model is established, and the prediction of the reservoir productivity under the logging resolution can be completed, so that the prediction accuracy is improved.
Description
Technical Field
The embodiment of the invention relates to the field of data processing in petroleum exploration, in particular to a method and a device for predicting the productivity of an oil and gas reservoir.
Background
The productivity refers to the capacity of the oil and gas reservoir to produce fluid in a stratum state, is basic data formulated by a reservoir productivity evaluation and development scheme, and is also an important parameter for determining the lower limit of the reservoir physical property. The reservoir productivity is determined by the conditions of the reservoir, engineering factors, oil and gas performance and the like, factors influencing reservoir geological features are complex and multi-aspect, the productivity prediction of the reservoir by applying complex parameters is very difficult, the methods such as the comprehensive productivity index and the reservoir category index have limitation and sidedness in the reservoir productivity prediction, the reservoir productivity cannot be accurately and quantitatively represented by a fine mathematical method, the prediction result is inaccurate, and the comprehensive evaluation is unreliable. Stratum testing is the most direct and effective method for verifying reservoir fluid properties and solving stratum productivity in oil and gas exploration at present, but because underground operation is difficult and high in cost and is only limited to local reservoir operation, a conclusion of capacity prediction needs to be provided as data support of stratum testing before stratum testing.
Disclosure of Invention
In view of the above, embodiments of the present invention are provided to provide a method and apparatus for hydrocarbon reservoir productivity prediction that overcomes or at least partially solves the above-mentioned problems.
According to an aspect of the embodiment of the invention, a method for predicting the productivity of a hydrocarbon reservoir is provided, and the method comprises the following steps:
preprocessing logging data and reservoir productivity data to construct a standardized sample set;
constructing and training a constraint neural network according to the sample set, and adding neurons in a constraint layer of the constraint neural network as constraint elements to constrain calculation of the constraint neural network; the input of the constraint element is calculated by a rock mechanics seepage model;
and acquiring logging data of the reservoir to be predicted, and inputting the logging data into the constraint neural network to obtain the reservoir capacity under unit pressure difference.
According to another aspect of the embodiments of the present invention, there is provided a hydrocarbon reservoir productivity prediction apparatus, including:
the system comprises a construction sample module, a storage capacity module and a data processing module, wherein the construction sample module is suitable for preprocessing logging data and reservoir productivity data to construct a standardized sample set;
the construction training module is suitable for constructing and training a constraint neural network according to the sample set, and adding neurons in a constraint layer of the constraint neural network as constraint elements so as to constrain the calculation of the constraint neural network; the input of the constraint element is calculated by a rock mechanics seepage model;
and the prediction module is suitable for acquiring logging data of the reservoir to be predicted and inputting the logging data into the constraint neural network to obtain the reservoir capacity under the unit pressure difference.
According to still another aspect of an embodiment of the present invention, there is provided a computing device including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the oil and gas reservoir productivity prediction method.
According to a further aspect of the embodiments of the present invention, there is provided a computer storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to perform operations corresponding to the hydrocarbon reservoir productivity prediction method.
According to the method and the device for predicting the productivity of the oil and gas reservoir provided by the embodiment of the invention, the constraint element is added into the constraint neural network, the constraint neural network under the constraint of the rock mechanics seepage model is established, and the prediction of the productivity of the reservoir under the logging resolution can be completed, so that the prediction accuracy is improved.
The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and the embodiments of the present invention can be implemented according to the content of the description in order to make the technical means of the embodiments of the present invention more clearly understood, and the detailed description of the embodiments of the present invention is provided below in order to make the foregoing and other objects, features, and advantages of the embodiments of the present invention more clearly understandable.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the embodiments of the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 illustrates a flow chart of a hydrocarbon reservoir capacity prediction method according to one embodiment of the present invention;
FIG. 2 illustrates a schematic diagram of a hydrocarbon reservoir capacity prediction apparatus according to one embodiment of the present invention;
FIG. 3 shows a schematic structural diagram of a computing device according to an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 1 shows a flow chart of a hydrocarbon reservoir productivity prediction method according to an embodiment of the present invention, as shown in fig. 1, the method includes the steps of:
and S101, preprocessing the logging data and the reservoir productivity data to construct a standardized sample set.
The reservoir productivity is determined by the conditions of the reservoir, engineering factors, oil and gas performance and the like, factors influencing reservoir geological characteristics are complex and multi-aspect, and the capacity prediction of the reservoir by applying complex parameters is very difficult. In the prior art, methods such as a comprehensive productivity index and a reservoir category index have limitation and one-sidedness in the productivity prediction of the oil and gas reservoir, and the reservoir productivity cannot be accurately and quantitatively represented by a fine mathematical method, so that the prediction result is inaccurate, and the comprehensive evaluation is unreliable.
In this embodiment, a sample set is constructed according to the logging data and the tested reservoir productivity data, so as to train the constructed productivity prediction neural network subsequently. Because the dynamic change rules of each phase fluid in the reservoir are different, the reservoir productivity including oil productivity and gas productivity are divided according to the properties of the reservoir fluid, and when a sample set is constructed, the oil productivity and the gas productivity need to be respectively divided into different types of data, and the sample set is constructed respectively. For example, a sample set is constructed by the logging data and the reservoir oil productivity data and is used for predicting the productivity prediction neural network of the oil productivity, and a sample set is constructed by the logging data and the reservoir gas productivity data and is used for predicting the productivity prediction neural network of the gas productivity.
When the productivity of the stratum is tested, the test conditions are different, and the productivity data of the stratum test reservoir are the productivity of a certain section of stratum, the constructed sample set also needs to be subjected to standardized processing so as to remove the limitation and one-sidedness of the constructed sample set. Specifically, the logging data are normalized according to the maximum value and the minimum value of the logging data, so that the response difference is in the same order, the metering units of the logging data are unified, and the regularity of the logging data can be better found during training. During normalization, if a certain logging data value is m, the maximum value is x, and the minimum value is y, normalization processing can be performed by using a (m-y)/(x-y) method, and normalization processing can be set according to implementation conditions, so that the logging data is normalized to a 0-1 interval, and the like, and a calculation method of the specific normalization processing is not limited herein. When the logging data are subjected to data preprocessing, one or more than one type of data preprocessing can be selected according to specific implementation conditions, so that the logging data have more regularity, and a neural network can be conveniently and accurately trained. In addition, abnormal values of the logging data are removed, for example, the abnormal values are determined according to technical means such as a rendezvous chart and the like, and the abnormal values are removed. Considering that the logging data have depth points with specified intervals, if the logging data do not contain the depth points corresponding to the reservoir productivity data, interpolating by using an interpolation method according to the adjacent upper and lower depths of the logging data to obtain the logging data of the depth points corresponding to the reservoir productivity data so as to complement the required default value. The above manner of calculating the logging data is for illustration, and the logging data of the required depth point is calculated according to the implementation, which is not limited herein. And corresponding to the reservoir productivity data, performing normalization processing on the reservoir productivity data, calculating to obtain the reservoir productivity under unit pressure difference, and dividing the reservoir productivity by the production pressure difference to obtain the reservoir productivity under the unit pressure difference. And calculating to obtain the productivity of the reservoir in the specified unit according to the average permeability of the specified unit, taking the average permeability of the specified unit as the average permeability of the meter, taking the average permeability of the meter as a weight coefficient, considering that the permeability of different stratums is different, and taking the productivity of the reservoir as the oil productivity for example, taking the ratio of the average permeability of the unit to the total permeability of the reservoir as the weight coefficient for the daily oil production y side of the reservoir with the thickness of x meters, multiplying the weight coefficient by the productivity of the reservoir, and calculating to obtain the productivity of the reservoir in the unit meter, namely the productivity of the reservoir in the specified unit. And (3) taking the logging data of the specified unit reservoir as the input data of the sample set, taking the productivity of the specified unit reservoir as the output data of the sample set, and constructing a standardized sample set to ensure the accuracy of the data in the sample set. For the sake of understanding, the oil production capacity is taken as an example, and the construction of the standardized sample set of oil production capacity and gas production capacity can be performed by referring to the above description, which is not limited herein.
And S102, constructing and training a constraint neural network according to the sample set.
The input layer of the constraint neural network is the logging data of the specified unit reservoir, and the output layer is the productivity of the specified unit reservoir. The constraint neural network is based on a conventional neural network structure and comprises an input layer, a constraint layer, a hidden layer and an output layer, wherein the constraint layer is a network layer appointed to increase constraint elements, and the constraint neural network is constructed by increasing the neuron in the constraint layer as the constraint element. Specifically, neurons are added to a constraint layer of the constraint neural network as constraint elements to constrain the computation of the constraint neural network. The input of the constraint element is calculated by a rock mechanics seepage model, the constraint element is in a non-connection state with the neuron of the input layer, and other neurons of the constraint layer are in a full-connection state with the neuron of the input layer. The input of the constraint element of the constraint neural network is provided by a rock mechanics seepage model, the output of the constraint element is fully connected with the next hidden layer, and the weight and the bias of the constraint element are jointly adjusted with other neurons. Compared with a conventional model, the constraint neural network has wider application range and higher calculation precision, and can well solve the problem of limitation and one-sidedness of the current oil and gas reservoir productivity prediction method.
Specifically, the constraint neural network has a specified number of layers, such as a neural network structure in which the businessman neural network has five layers, the second specified layer is a full-link layer, and if the first two layers are full-link layers, the number of neurons in the first layer is twice that of neurons in the second layer, and the first layer is used for feature extraction. The number of neurons in the third layer is halved on the basis of the second layer, and an input neuron is added as a constraint element, wherein the constraint element is obtained by calculating a rock mechanics seepage model, and the rock mechanics seepage model can adopt the following formula:
wherein q is a constraint element, k is the absolute permeability contained in the logging data, and k isrIn terms of relative permeability, μ is viscosity, B is volume factor, reIs an equivalent radius, rwIs the wellbore radius and S' is the apparent skin coefficient. And the constraint element is obtained by calculation of a rock mechanics seepage model, wherein k is the absolute permeability contained in the logging data in the sample set and can be directly input in an input layer, and other parameters are directly input when a constraint neural network is constructed. The number of the neurons in the fourth layer is twice that of the neurons in the third layer, and the neurons in the fourth layer are fully connected with the neurons in the third layer and used for refining data; and the last layer is an output layer and is used for outputting the productivity of the specified unit reservoir. The above formula is an example, and in specific implementation, a suitable formula corresponding to the rock mechanical seepage model may be selected according to implementation conditions, which is not limited herein.
The sample set adopted by the constraint neural network is biased to the stratum with better productivity, if no constraint element is added on the constraint layer, the conventional neural network is used for accurately predicting the stratum with better productivity and poorly predicting the stratum with poorer productivity, the formula is used for regularly processing the stratum with better productivity or the poorer productivity, and the reservoir productivity obtained by calculation of the formula is combined with the constraint neural network, so that the regularity of the constraint neural network is more accurate, and the constraint neural network is suitable for various stratums.
During training, a K-Fold cross validation mode can be adopted to divide the sample set into a test sample set and a validation sample set for training. And inputting the sample set into a constraint neural network, inputting the reservoir productivity obtained by calculation by using a formula as an increased constraint element into a third layer according to the permeability and each parameter contained in the logging data of the sample set when the third layer is used, training and adjusting the training parameters of the constraint neural network, and finishing the training of the constraint neural network.
The model of the constraint neural network comprises a region model, a general model and the like, and the type is determined according to the quantity, the size, the distribution range and the like of the sample set. And selecting and constructing a proper type of constraint neural network according to implementation conditions.
And step S103, acquiring logging data of the reservoir to be predicted, inputting the logging data into the constraint neural network, and obtaining the reservoir capacity under unit pressure difference.
And acquiring logging data to be predicted, and inputting the logging data to the constrained neural network obtained by training to obtain a prediction result. The prediction result comprises a reservoir productivity curve under the unit pressure difference logging resolution. The energy storage capacity of the stratum is related to the production pressure difference, and the reservoir capacity is different under different production pressure differences. And when the method is actually applied, accumulating the reservoir productivity curve under the unit pressure difference logging resolution according to the production pressure difference and the depth range of the interval to be predicted, multiplying the accumulated result by the actual production pressure difference, and calculating to obtain the reservoir productivity of the interval to be predicted.
According to the method for predicting the productivity of the oil and gas reservoir provided by the embodiment of the invention, the constraint element is added into the constraint neural network, the constraint neural network under the constraint of the rock mechanics seepage model is established, and the prediction of the productivity of the reservoir under the logging resolution can be completed, so that the prediction accuracy is improved.
Fig. 2 shows a schematic structural diagram of a hydrocarbon reservoir productivity prediction device provided by the embodiment of the invention. As shown in fig. 2, the apparatus includes:
the construction sample module 210 is suitable for preprocessing logging data and reservoir productivity data to construct a standardized sample set;
the construction training module 220 is adapted to construct and train a constraint neural network according to the sample set, and add neurons as constraint elements in a first designated layer of the constraint neural network to constrain the calculation of the constraint neural network; the input of the constraint element is calculated by a rock mechanics seepage model;
and the prediction module 230 is suitable for acquiring logging data of the reservoir to be predicted, inputting the logging data into the constraint neural network and obtaining the reservoir capacity under the unit pressure difference.
Optionally, the construct sample module 210 is further adapted to:
carrying out normalization processing on the logging data, eliminating abnormal values, and complementing default values by using an interpolation method;
carrying out normalization processing on the reservoir productivity data, and calculating to obtain the reservoir productivity under unit pressure difference;
calculating to obtain the productivity of the specified unit reservoir according to the average permeability of the specified unit;
and (3) constructing a standardized sample set by taking the logging data of the specified unit reservoir as input data of the sample set and the capacity of the specified unit reservoir as output data of the sample set.
Optionally, the constraint neural network structure is composed of an input layer, a constraint layer, a hidden layer and an output layer, wherein the constraint layer is a network layer for adding constraint elements.
Optionally, the constraint element of the constraint layer is in a non-connected state with the neuron of the input layer, and the other neurons of the constraint layer are in a fully connected state with the neuron of the input layer.
Optionally, the constrained layer is constrained by a rock mechanical seepage model, and the constrained element of the constrained layer is calculated by using the rock mechanical seepage model.
Optionally, the input of the constraint element of the constraint neural network is provided by a rock mechanics seepage model, and the output is fully connected with the next hidden layer; the weight and bias of the constraint element are adjusted together with other neurons.
Optionally, the model of the constrained neural network comprises a region model and/or a general model; the type is determined according to the number size and/or distribution range of the sample set.
The descriptions of the modules refer to the corresponding descriptions in the method embodiments, and are not repeated herein.
The embodiment of the invention also provides a nonvolatile computer storage medium, wherein the computer storage medium stores at least one executable instruction, and the executable instruction can execute the oil and gas reservoir productivity prediction method in any method embodiment.
Fig. 3 is a schematic structural diagram of a computing device according to an embodiment of the present invention, and a specific embodiment of the present invention does not limit a specific implementation of the computing device.
As shown in fig. 3, the computing device may include: a processor (processor)302, a communication Interface 304, a memory 306, and a communication bus 308.
The method is characterized in that:
the processor 302, communication interface 304, and memory 306 communicate with each other via a communication bus 308.
A communication interface 304 for communicating with network elements of other devices, such as clients or other servers.
The processor 302 is configured to execute the program 310, which may specifically execute the related steps in the above-mentioned method for predicting hydrocarbon reservoir productivity.
In particular, program 310 may include program code comprising computer operating instructions.
The processor 302 may be a central processing unit CPU, or an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement an embodiment of the present invention. The computing device includes one or more processors, which may be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And a memory 306 for storing a program 310. Memory 306 may comprise high-speed RAM memory and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 310 may be specifically configured to cause the processor 302 to perform the hydrocarbon reservoir capacity prediction method in any of the method embodiments described above. The specific implementation of each step in the procedure 310 may refer to the corresponding steps and corresponding descriptions in the units in the above oil and gas reservoir productivity prediction embodiments, which are not described herein again. It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described devices and modules may refer to the corresponding process descriptions in the foregoing method embodiments, and are not described herein again.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of embodiments of the present invention as described herein, and any descriptions of specific languages are provided above to disclose preferred embodiments of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that is, the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components according to embodiments of the present invention. Embodiments of the invention may also be implemented as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing embodiments of the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of the invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specified otherwise.
Claims (10)
1. A method for predicting the productivity of an oil and gas reservoir is characterized by comprising the following steps:
preprocessing logging data and reservoir productivity data to construct a standardized sample set;
constructing and training a constraint neural network according to the sample set, and adding neurons in a constraint layer of the constraint neural network as constraint elements to constrain calculation of the constraint neural network; the input of the constraint element is obtained by calculation of a rock mechanics seepage model;
and acquiring logging data of the reservoir to be predicted, and inputting the logging data into the constraint neural network to obtain the reservoir capacity under the unit pressure difference.
2. The method of claim 1, wherein the pre-processing the well log data and the reservoir productivity data and constructing the standardized sample set further comprises:
carrying out normalization processing on the logging data, eliminating abnormal values, and complementing default values by using an interpolation method;
carrying out normalization processing on the reservoir productivity data, and calculating to obtain the reservoir productivity under unit pressure difference;
calculating to obtain the productivity of the specified unit reservoir according to the average permeability of the specified unit;
and constructing a standardized sample set by taking the logging data of the specified unit reservoir as input data of the sample set and taking the productivity of the specified unit reservoir as output data of the sample set.
3. The method of claim 1, wherein the constraint neural network structure is composed of an input layer, a constraint layer, a hidden layer, and an output layer, wherein the constraint layer is a network layer that specifies adding constraint elements.
4. The method of claim 3, wherein the constraint elements of the constraint layer are in a non-connected state with the neurons of the input layer, and wherein the other neurons of the constraint layer are in a fully connected state with the neurons of the input layer.
5. The method of claim 3, wherein the constrained layer is constrained by a rock mechanical seepage model, and the constrained elements of the constrained layer are calculated using the rock mechanical seepage model.
6. The method of claim 5, wherein the input of the constraint element of the constraint neural network is provided by a rock mechanical seepage model, and the output is fully connected with the next hidden layer; the weight and bias of the constraint element are adjusted together with other neurons.
7. The method of claim 1, wherein the model of the constrained neural network comprises a region model and/or a general model; the type is determined according to the number size and/or distribution range of the sample sets.
8. An oil and gas reservoir productivity prediction device, characterized in that the device comprises:
the system comprises a construction sample module, a storage capacity module and a data processing module, wherein the construction sample module is suitable for preprocessing logging data and reservoir productivity data to construct a standardized sample set;
the construction training module is suitable for constructing and training a constraint neural network according to the sample set, and adding neurons in a constraint layer of the constraint neural network as constraint elements to constrain the calculation of the constraint neural network; the input of the constraint element is obtained by calculation of a rock mechanics seepage model;
and the prediction module is suitable for acquiring logging data of the reservoir to be predicted and inputting the logging data into the constraint neural network to obtain the reservoir capacity under the unit pressure difference.
9. A computing device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the oil and gas reservoir capacity prediction method according to any one of claims 1-7.
10. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the hydrocarbon reservoir productivity prediction method of any one of claims 1-7.
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