AU2020102001A4

AU2020102001A4 - Method for Identifying and Describing Deep Carbonate Karst Structures

Info

Publication number: AU2020102001A4
Application number: AU2020102001A
Authority: AU
Inventors: Xiaojie GENG; Hao Li; Changsong Lin
Original assignee: China University of Geosciences Beijing
Current assignee: China University of Geosciences Beijing
Priority date: 2020-06-30
Filing date: 2020-08-26
Publication date: 2020-10-01
Anticipated expiration: 2028-08-26
Also published as: CN111766629A; CN111766629B

Abstract

The present invention discloses a method for identifying and describing deep carbonate karst structures, which comprises the following steps: identifying a karst development site in a target interval of a well point in a target area; capturing and interpreting an imaging logging image to determine the specific depth range of karst structure development in the target interval; identifying the characteristics of each constituent unit of the karst structure, including characteristics of the rock core, characteristics of the imaging logging image, and response characteristics of a conventional logging curve. The method for identifying and describing deep carbonate karst structures in the present invention utilizes rock core, imaging logging image and natural gamma curve in combination, and can identify the types of deep carbonate karst structural units more accurately. 1/15 Determine the karst development interval on the basis of seismic reflection characteristics Acquire imaging logging images of the target interval Calibrate the imaging logging images with rock core sample data Establish an identification template for imaging logging images of karst structural units Establish a vertical development model of karst structure by means of combinations of imaging logging image units Fig. 1 Static image Dynamic image Rock core Depth GR image 5562 Fig.lIA

Description

1/15

Determine the karst development interval on the basis of seismic reflection characteristics

Acquire imaging logging images of the target interval

Calibrate the imaging logging images with rock core sample data

Establish an identification template for imaging logging images of karst structural units

Establish a vertical development model of karst structure by means of combinations of imaging logging image units

Fig. 1

Static image Dynamic image Rock core Depth GR image

5562

Fig.lIA

AUSTRALIA

Patents Act 1990

COMPLETE SPECIFICATION

Invention title:

"METHOD FOR IDENTIFYING AND DESCRIBING DEEP CARBONATE KARST STRUCTURES"

Applicant:

CHINA UNIVERSITY OF GEOSCIENCES, BEIJING

Associated provisional applications:

The following statement is a full description of the invention, including the best method of performing it known to me:

"METHOD FOR IDENTIFYING AND DESCRIBING DEEP CARBONATE KARST STRUCTURES"

Field of the Invention

[0001] The present invention relates to the technical field of carbonate karst detection, in particular to a method for identifying and describing deep carbonate karst structures.

Background to the Invention

[0002] Delicate analysis of karst structures and research on the complex karst formation mechanism are the focus of attention of geologists at home and abroad as well as one of the hot issues to be solved urgently in carbonate oil and gas exploration. A large number of scholars have studied the general characteristics, formation and evolution of carbonate karst structures on the basis of field outcrop data and oilfield underground data, but their understanding on karst macrostructures is not clear enough at present. Scientific and systematic characterization of deep carbonate karst structures is an important basis for revealing the deep carbonate rock formation mechanism.

[0003] Imaging logging is a logging technique emerging in recent years, which overcomes the drawbacks of low resolution of seismic profile, low success ratio of deep core sampling and low accuracy of conventional logging, and has become the most intuitive and accurate means reflecting underground formation information. With the imaging logging technique, a large number of intuitive images information can be provided for analyzing sedimentary tectonic characteristics of the formation, and information on fractures, karst caves and dissolution pores formed in carbonate formation can be extracted for semi-quantitative analysis. Although the application of imaging logging is popularized increasingly, there are still few methods for describing deep carbonate karst structural units comprehensively and finely, which are mainly based on imaging logging data and supplemented by other data.

[0004] A deep karst structure model can be established through observation of modern karst or paleokarst outcrops.

[0005] Karst reservoirs are an important target for oil and gas exploration in Ordovician carbonate rocks in the world. Different from modern karsts, deep paleokarsts have the characteristics of great burial depth and complex structure. Delicate analysis of karst structures and research on the complex karst formation mechanism are the focus of attention of geologists at home and abroad as well as one of the hot issues to be solved urgently in carbonate oil and gas exploration.

[0006] In recent 20 or 30 years, based on modern karst process and outcrop analysis, a large number of scholars have carried out extensive research on karst structures and their controlling factors, and have established various evolution models to explain the formation process of deep carbonate karst structures. Based on modern karst and field outcrops, important understandings have been obtained in the research of karst cave structures. Karst caves are categorized into funnel type, tubular branch type, plate-like bedding distribution type, cave group type and other types, in terms of the shapes and scales of karst caves; karst caves may be mainly categorized into three filling types: cracked breccia filling, mixed breccia filling and cave deposition filling, in terms of the filling of karst caves; in terms of karst caves and their associated structures, focus is set to the important role of breccia and associated faults and fractures formed by karst cave collapse in the karst structures. An integrated pore network system composed of karst caves at different scales and associated dissolution pores, fractures and cave collapse breccia provides important oil and gas reservoir spaces.

[0007] Deep karst structures are depicted through seismic analysis, conventional logging and core sampling.

[0008] With the enrichment of underground data, seismic analysis and conventional logging can be used to identify the deep karst system. A karst reservoir has characteristic response of bead-like or horizontal strong reflection on seismic reflection. Through interpretation of cross-well profiles of different karst geomorphic units, it is found that there are certain rules of distribution of the reflection characteristics on paleogeomorphic units. Wherein, bead-like reflection mostly occurs in karst highland and steep karst slope areas where the degree of denudation is high, and the reflection in slope areas is mainly abnormal strong parallel reflection. In karst depression areas, the karstification is very weak, and there is no special seismic reflection characteristic. The differences in seismic reflection characteristics reflect the internal differences among karst structures to a certain extent. General speaking, caves in 0.5-5m height don't result in any obvious anomaly in seismic and drilling data, while caves in height greater than 5m are revealed in 3D seismic data. Owing to the limitation of seismic resolution, the seismic profile can't exhibit the entire external morphology of karst caves encountered in the drilling process in the vertical direction.

[0009] Seismic data can only describe the overall seismic reflection anomaly of a karst zone owing to the limited accuracy of seismic data. More accurate data, such as rock core sampling data and logging data, etc., is required to accurately describe the characteristics of the internal of a karst zone. However, the drilling and core sampling cost of deep carbonate rocks is very high. From the viewpoint of production, it is impractical to carry out core sampling for the entire well section. Especially, it is very difficult to drill and take a core sample since the pressure is released when a karst cave is encountered in the drilling process. Even if core samples of different well sections are available, the core samples may not be representative to reflect the overall characteristics of the formation, owing to the influence of core sampling ratio and accuracy of locating the cores.

Summary of the Invention

[0010] The technical problem to be solved by the present invention is to at least partially overcome the existing drawbacks in the prior art by providing a method for identifying and describing deep carbonate karst structures, which utilizes core samples, imaging logging images and a natural gamma curve in combination to identify the types of deep carbonate karst structural units more accurately, and can effectively solve problems in the background art.

[0011] To solve the technical problems described above, the present invention provides the following technical scheme.

[0012] The present invention provides a method for identifying deep carbonate karst structures, which comprises the following steps: Si: identifying a karst development site in a target interval of a well point in a target area; S2: capturing and interpreting an imaging logging image to determine the specific depth range of karst structure development in the target interval; S3: identifying the characteristics of each constituent unit of the karst structure, including characteristics of the rock core, characteristics of the imaging logging image, and response characteristics of a conventional logging curve.

[0013] As a preferred scheme, the step S1 comprises: selecting a seismic profile at the well point in the target area, making well-seismic comparison, calibrating the level of the target interval, determining the position of the target interval on the seismic profile, and identifying the characteristics of the seismic reflection in-phase axis of the target interval, wherein the presence of a vertical "bead-like" reflection zone or a reflection zone with laterally enhanced amplitude indicates that there is a development of karst caves.

[0014] As a preferred scheme, the step S2 comprises: identifying the karst structure at the corresponding position on the imaging logging image according to the position of the target interval calibrated through well-seismic comparison in the step S1: specifically, interpreting the imaging logging image of coring interval of the target interval of a single well where a rock core is taken, and calibrating the rock structure and tectonic characteristics of the cores at the same depth with the imaging logging image, so as to obtain the accurate depth of the target structures as well as the presentation forms of various karst structures in the imaging logging image.

[0015] As a preferred scheme, the step S3 comprises: using the characteristics of the imaging logging image and a conventional logging curve, especially a natural gamma curve, in combination to identify each karst structural unit.

[0016] As a preferred scheme, the identifying method comprises: 1) identifying the filling characteristics in a karst cave, including the accumulation characteristics of filling materials, the arrangement pattern of filling breccia, and the relative mud content in the filling materials, according to the morphological and color characteristics of the imaging logging image of the karst cave development interval in conjunction with the natural gamma value; 2) after the images of different karst structural units are identified, further carrying out fine division according to the differences of natural gamma value, neutron porosity, acoustic wave logging value, lithologic density logging value and conventional resistivity logging value; 3) for a well point without imaging logging image, identifying whether the formation is karst formation or non-karst formation through statistical analysis of conventional logging values: specifically, five sensitivity parameters, including lithologic density logging parameter, neutron porosity logging parameter, mudstone content logging parameter, shallow lateral conductivity and absolute value of difference between bilateral conductivity, can be selected and used for pairwise cross-plotting, and determining whether the formation is karst formation or non-karst formation according to the distribution range of the logging values on the cross plot diagram.

[0017] A method for describing deep carbonate karst structures, comprising the following steps: Si: dividing the karst structure of a single well: dividing the vertical composition of the karst structure of a single well, according to the type of each karst structural unit determined with the above scheme and the characteristics of the corresponding imaging logging image; S2: comparing the karst profiles of successive wells: selecting the profiles of successive wells in multiple directions in the area of interest, comparing the karst structural units among the successive wells and dividing karst evolution stages, on the basis of the division of the karst structure of each single well; S3: with reference to the characteristics of the imaging logging images of each karst unit obtained in step 2, selecting a typical single well, interpreting the imaging logging image of the karst development intervals of the single well, and analyzing the combination forms, distribution types and characteristics of the karst structural units in the vertical direction; S4: after analyzing the karst characteristics of typical single wells in the vertical direction, selecting the profiles of multiple successive wells in transverse and longitudinal directions in the target area and carrying out cross-comparison, and analyzing the evolution stages of the karst structures with reference to the regional evolution characteristics of the target area.

[0018] One or more technical schemes provided by the present invention have at least the following technical effects or advantages: (1) The present invention utilizes rock core, imaging logging image and natural gamma curve in combination in the karst structural unit identification process, and can identify the types of deep carbonate karst structural units more accurately. (2) In the identification of karst structural units, a large number of imaging logging images are used to finely depict karst structural units for wells without core samples, thus accuracy of identification of karst structural units is improved. (3) In the process of dividing the karst structure units of a single well, the accuracy of identification of longitudinal karst structural units of the single well and their combinations is improved with the method for identifying karst structural units provided by the present invention.

[0019] As used herein, unless the context requires otherwise, the verb 'to comprise' in its various tenses is used in an 'inclusive' sense: that is, its use implies the inclusion of a stated integer or group of integers, but does not imply the exclusion of any other integer.

Brief Description of the Drawings

[0020] The accompanying drawings are provided to help further understanding of the present invention, and constitute a part of the description. These drawings are used in conjunction with the embodiments in the present invention to interpret the present invention, but don't constitute any limitation to the present invention. In the figures:

[0021] Fig. 1 is a schematic flow chart of the method for identifying deep carbonate karst structures in an embodiment of the present invention;

[0022] Fig. 1A is a diagram of rock core calibration based on imaging logging images in the method for identifying deep carbonate karst structures in an embodiment of the present invention;

[0023] Fig. 1B is another diagram of rock core calibration based on imaging logging images in the method for identifying deep carbonate karst structures in an embodiment of the present invention;

[0024] Fig. 2A is a schematic diagram of static image of an eroded formation in the method for identifying deep carbonate karst structures in an embodiment of the present invention;

[0025] Fig. 2B is a schematic diagram of static image of another eroded formation in the method for identifying deep carbonate karst structures in an embodiment of the present invention;

[0026] Figs. 3A, 3B, 3C, 4A, 4B, 4C, 5A, 5B, 6A, 6B, 7A, 7B, 7C, 8 are schematic diagrams of finely identifying the characteristics of the structural unit of a karst system by using dynamic images and static images in combination in the method for identifying deep carbonate karst structures in the embodiments of the present invention;

[0027] Figs. 9, 10, 11 and 12 are schematic diagrams of different longitudinal combinations of various images in the method for identifying deep carbonate karst structures in the embodiments of the present invention; and

[0028] Fig. 13 is a schematic diagram of Yingshan Formation in a total thickness of 168m encountered in the drilling in a paleokarst slope in the west of the northern slope of Tazhong uplift, identified with the method for identifying deep carbonate karst structures in the embodiments of the present invention.

Detailed Description of Preferred Embodiments

[0029] To make the objects, technical scheme and advantages of the present invention understood more clearly, hereunder the present invention will be further detailed in embodiments, with reference to the accompanying drawings. It should be understood that the embodiments described hereunder are only provided to interpret the present invention but don't constitute any limitation to the present invention.

[0030] To make the above technical scheme understood better, hereunder the above technical scheme will be detailed in an embodiment with reference to the accompanying drawings.

Example:

[0031] Please see Fig. 1. This example provides a method for identifying deep carbonate karst structures, which comprises the following steps:

S1: identifying a karst development interval by means of a seismic profile

[0032] General speaking, caves in 0.5-5m height don't result in any obvious anomaly in seismic and drilling data, while caves in height greater than 5m are revealed in 3D seismic data. With Landmark software, a typical seismic profile in the area of interest is interpreted. Owing to the limitation of seismic resolution, the external morphologies of the karst caves encountered in the process of drilling in the vertical direction can't be fully reflected on the seismic profile; instead, a characteristic response of bead-like or horizontal strong reflection is exhibited on the seismic profile, and the characteristic response is also a main seismic identification characteristic of large-size supergene karst caves. The specific depth of the karst development interval can be obtained by cross-calibration of the logging curve and the seismic profile.

S2: acquiring imaging logging images of the target interval

[0033] Imaging logging usually continuously logs an entire well interval or target interval. The imaging logging tool records a digital matrix of resistivity via multiple electrodes at the same time; then, through a series of digital and image processing, such as data recovery, image generation and image enhancement, the digital matrix of resistivity is converted into two-dimensional images represented by color codes, including static image and dynamic image, which reflect the relative resistivity of the wellbore formation. The color codes are black-brown-yellow-white colors, which represent the change of resistivity from low to high; wherein in the static image, the entire logged interval is represented with the same color code, and is usually used to reflect the overall change of lithology; the dynamic image is obtained by normalizing the image in a small range, and highlights the detailed change of the formation in a small range. The imaging logging image is usually displayed as a plane development diagram. That is to say, the image is developed along the wellbore from true north and projected on a plane in a sequence from left to right and from north (00) to east (900), south (1800), west (2700) and north (3600). According to the result of well-seismic calibration in the first step, the karst development interval is extracted for fine imaging logging interpretation. The imaging logging interpretation is carried out, utilizing a natural gamma curve, because natural gamma logging is sensitive to the response of mud, and usually exhibits an abnormal high natural gamma value in a case that the karst development interval involves mud filling. Utilizing the natural gamma curve, the characteristics of the filling material in the karst system can be judged accurately, and thereby the formation mechanism can be interpreted.

S3: calibrating the imaging logging images with core samples

[0034] The resolution scale of imaging logging images is essentially the same as that of rock cores. Therefore, in order to ensure the rationality and accuracy of imaging logging interpretation, firstly, the corresponding imaging logging images are calibrated with limited core sample data, and the imaging logging images are used to direct the true depth determination of the rock cores, and the rock core samples are used to depict the types of the imaging logging images, so as to more accurately interpret the imaging logging images without corresponding rock core samples.

[0035] For example, the surrounding rock in Fig. 1A is light gray intraspararenite, with small karst caves filled by breccia in the upper part. The core sample indicates that the bottoms of the caves have obvious erodsion surface characteristics, the breccia and dark mudstone filled in the caves developed into horizontal bedding, and the breccia is arranged directionally. The corresponding imaging logging image indicates that there is an abrupt characteristic change between the upper image above the contact surface and the lower image below the contact surface, wherein the upper part is alternately dark and bright bedding strips formed by filled karst caves, while the lower surrounding rock has a high resistance massive characteristic. The natural gamma curve exhibits an increasing trend on the filling layer of karst caves, which is a response to the increasing mud content in the filling material.

[0036] In Fig. 1B, a small karst cave is encountered in the drilling in the rock core at 5,560m depth, the materials in the cave are broken, filled calcite breccia is found, and the resistivity is obviously different from that of the light gray intraspararenite of the surrounding rock. In the static image of imaging logging, it can be seen that the part corresponding to karst cave is dark brown massive response; in the dynamic image, it can be seen that there are low resistance dark material and high-resistance bright white breccia material (calcite breccia) in the filling materials in the cave. On the rock core sample, fractures filled by dark mud are seen at the bottom of the cave, and correspond to the dark strips in the bright yellow background in the imaging logging image. Besides, the response of the natural gamma logging curve to the karst cave and surrounding rock also has a sudden decreasing trend.

S4: establishing an identification template for imaging logging images of karst structural units

[0037] The imaging logging data is processed to obtain the image information of the logging data, including dynamic image and static image. The image information includes image structure and color. The static image is obtained through normalization for the entire measured interval, and can provide absolute reference for lithology interpretation; the dynamic image is obtained through normalization in a small range with a dynamic image enhancement algorithm, for the purpose of highlighting the fine characteristics of the formation.

[0038] The static image of an eroded formation is brown-dark brown, and has a clear boundary with the image of a non-eroded formation in color. A non eroded formation usually has the following two image characteristics:

[0039] Both the static image and the dynamic image shows bright yellow massive structures (Fig. 2A), with great overall thickness and few dark strips inside. A high-resistance bright massive image often represents micrite limestone and algae-bound limestone, etc. deposited stably in a low energy environment, with dense lithology, great thickness and poor dissolution, and usually acting as an impermeable barrier in the karst system.

[0040] A bright high-resistance horizontal strip image (Fig. 2B): the overall color of the static image is bright yellow-orange, with alternative deep and light strips. The strip thickness is about 0.1m, there is no special drilling response, the natural gamma curve is extremely low; the cyclic change of energy of the sedimentary water body results in the change of sedimentary lithology and resistivity; the overall resistivity is low, and acting as an impermeable barrier in the karst system.

[0041] The imaging logging images are interpreted, and the deep carbonate karst structural units are identified according to the image characteristics.

After the karst system is identified on the static image, the structural characteristics of the constituent units of the karst system are accurately identified by utilizing the dynamic image and the static image in combination, and are represented by the following 11 types of images:

[0042] ©D The static image shows dark brown-black massive structures (Fig. 3A), with uniform color distribution and no obvious bedding characteristic, usually in thickness of 1-10m; the dynamic image reveals subtle changes and some small bright spots can be seen occasionally. The dark massive facies represents the response of mud-filled caves;

[0043] @ The static image is generally dark (Fig. 3B), usually in light brown-brown color, with parallelly distributed dark strips in thickness of about 0.1m or greater; from the dynamic image, it can be seen that the dark strips may be thin layers developed from honeycomb pores or small caves filled by mud;

[0044] @ Alternative bright and dark strips distributed vertically (Fig. 3C): unlike horizontal strips, vertical strips are a special form presented due to the formation collapse and slippage during drilling, usually accompanied by drilling fluid leakage and increased drilling time, exhibiting overall logging response characteristics of low resistance and high gamma value; or may be a result of sudden change of drilling rate when karst caves are encountered during the drilling;

[0045] @ Light and dark thin layers are interbedded in an overall dark low resistance background (Fig. 4A), with high layer density and layer thickness smaller than 0.1m. This type of image is often obtained near an unconformity plane of the formation, mostly located at the upper part of a dark massive image, representing the response of paleosoil near the unconformity plane or muddy filling material at the top of a large-size karst cave;

[0046] @ Bright patches randomly distributed in a bright background (Fig. 4B), in different sizes, with fuzzy boundaries and the arrangement thereof does not have sequentiality, the patches are locally broken karst breccia, filled by low-resistance mud among the breccia, and are formed through compaction and contact of the breccia in the later compaction process;

[0047] @ The image exhibits bright patches randomly distributed in a dark background (Fig. 4C), with lower overall resistivity. Especially, randomly distributed high-resistance patches in different sizes are seen in the dynamic image. The karst cave is filled by breccia of the surrounding rock, and the mud content in the spaces among the breccia is high, and the breccia may be partially transported during the movement of underground fluids;

[0048] © Bright and orderly arranged patches in a dark background (Fig. A): the bright patches are arranged orderly, with clear patch boundaries and in uniform size. The karst breccia were not transported, but fractured and deposited in situ, in line contact with each other. The dark filling among the breccia is usually resulted from fracture development at the top of the karst cave without collapse;

[0049] @ Small pinhole or honeycomb dark spots in a bright background (Fig. 5B), in various shapes and disordered distribution, representing small dissolution pores, which are usually associated with diffused fractures; sometimes layered spots occur because the pinhole spots are distributed in layers at meter level, usually in thickness of about 1m, these pinhole spot layers are dissolution pore layers, which are usually separated by bright interlayers;

[0050] @ Dark sinusoidal curves distributed nearly parallel in a bright yellow background (Fig. 6A): these sinusoidal curves are high-angle fractures, and the thickness of the curves is uneven, wherein the thickened portions occur often due to different degrees of diffusion of dissolution along the fracture surfaces;

[0051] @ Bright-color interlaced mesh image (Fig. 6B), generally in a bright color, with interlaced sinusoidal curves of different amplitudes, representing structural fractures developed in different stages, in an open or mud-filled state. This image usually represents the main response characteristics of a seepage zone in the karst system, and the fractures can be categorized into high-angle fractures, low-angle fractures and mesh fractures according to their occurrence (Fig. 7), wherein low-angle fractures appear as gentle dark sinusoidal curves in the imaging logging image, high angle fractures appear as high and steep sinusoidal curves in the imaging logging image, while mesh fractures are usually composed of more than two sets of sinusoids crossing each other and appear as mesh structures in the imaging logging image (Figs. 6A, 6B, 7A, 7B, 7C and 8).

[0052] @ Dark unidirectional mesh image (Fig. 8), with a dark background usually in brown-dark brown color, and essentially vertical black strips distributed essentially in parallel. It is considered that vertical fractures are densely distributed, and often accompanied by randomly distributed dissolution pores, with very low overall resistivity. This facies type is the most favorable reservoir development facies type.

S5: establishing a vertical development model of the karst structure by combining the imaging logging image units

[0053] Karst caves are the main structures of a karst system. Karst caves, fractured formation associated in the upper part and lower part of the karst caves, and filling materials inside the karst caves jointly constitute the vertical structure of a karst system, and are usually displayed as vertical combinations of different types of images in different ways in the imaging logging image.

[0054] Please see Fig. 9. The static image exhibits a dark massive structure, which has a clear boundary with the upper and lower formation in image color; at the bottom of the cave, the filling material comes into abrupt contact with the undisturbed bottom layer, and the imaging logging image changes from dark brown to bright yellow. It can be judged as a cave with about 12m vertical depth; then, the filling characteristics of the cave can be analyzed with the dynamic image: the two ends in the deepest color are dark massive images, representing two mud-filled caves; and the lower dark massive image has floating bright spots, which are carbonate breccia filled in the karst cave.

[0055] Please see Fig. 10, which shows a two-section filled karst cave mainly filled by breccia, with 25m vertical depth, and the dark layered image in larger size at 5100m represents the response characteristics of low-resistance paleosoil near the unconformity plane at the top of the cave. The imaging logging image at about 12m below the dark layered image exhibits a dark massive structure. The structure at 5101m to 5116m is an upper cave, and the top at 5,101m to 5,107m corresponds to a dark massive facies, which is a cave section filled by a material having high mud content. Although broken breccia can be seen in the dynamic image, the breccia have blur boundaries and are different in size. Apparently the breccia have been eroded and even abraded by mud and sand filling materials for many stages, thus the breccia have complex composition, with sharply increased retained mud content, resulting in response characteristics of extremely low resistance and extremely high natural gamma value. The structure at 5107m to 5112m has a natural gamma value lower than that of the upper structure, and broken surrounding rock breccia can also be distinguished in the static image, the breccia is in a floating distribution form in the muddy filling material; though the mud content is lower than that in the upper structure, the filling material is still mainly a muddy material. The structure at 5113m to 5115m is a structure pilled and filled by breccia in a thickness of 3m, mainly represented by images of high-resistance orderly arranged patches in a low-resistance background. The boundaries of breccia can be clearly distinguished in the static image and the dynamic image, the breccia is in a surface contact relationship, and there are fractures filled by mud among the breccia. From the straight low-value natural gamma curve, it can be judged that the content of mud filling material among the fractures is extremely low; the broken breccia at the bottom of the cave has no obvious displacement. The cave at 5,116m to 5,122m is a cave in another stage, the top of the cave is shown as a low-resistance unidirectional mesh image, the cave communicates with the upper cave through a group of essentially vertical factures and serves as a flow channel of underground fluids. The cave in this stage is mainly filled by mud, with a small amount of floating high-resistance bright breccia in the filling material. The formation is the original surrounding rock formation again from 5,122m.

[0056] Please see Fig. 11. The vertical depth of the cave is about 1Om. According to the display of the imaging logging image, the imaging logging facies at 6,126m to 6,127m is a dark low-resistance massive image, which represents the mud filling material at the top of the karst cave, with only a small amount of floating high-resistance breccia. The structure at 6,128m to 6,132m is the main part of the karst cave, which is a dissolved breccia section at a high dissolution degree of breccia, the fractures among the breccia are filled by mud, and the natural gamma value is higher than medium (about API). The image at 6,131m to 6,132m is a low-resistance massive image, with increased mud content, forming a small peak in the natural gamma curve. The karst cave is small in scale, and the filling materials are not filled orderly. The mud content decreases gradually starting from the top of the cave, and the natural gamma curve has a general trend of falling from high to low. In this type of breccia filling, the composition of the breccia is usually simple without grading. The spaces among the breccia are filled by mud and calcite, and the mud flows down through the fractures among the breccia and deposited at the bottom of the cave.

[0057] Please see Fig. 12. The vertical depth of the cave is about 12m, and the main part of the karst cave is at 5,263m to 5,273m. A group of high-angle fractures are developed in the original bottom layer of the cave roof, and may be the result of cave collapse and gravity unloading. The cave filling materials may be divided into three sections from top to bottom, namely: (1) Alternative light and dark layer images in the imaging logging image, representing the top layer filled by muddy sand and developed into horizontal bedding; (2) Bright patches image (at 5,268m to 5,271m): a floating breccia filled section. The breccia are stacked more loosely from bottom to top, the mud content is increased, and the contact form of the breccia varies from surface contact at the lower part to floating form at the upper part;

(3) Dark massive image, representing a mud filled section at the bottom of the cave, the deposited mud filing layer is in thickness of about 2m.

[0058] Owing to the difference in the formation mechanism between the paleokarst exposed in the field and the paleokarst developed in the deep formation, it is impossible to directly infer the structural characteristics of the deep karst from the observation result of the field outcrop; the main data of deep formation, such as seismic and core data, have some limitations in analyzing karst structures. Therefore, a large number of high-precision imaging logging images become important data for analyzing deep carbonate karst structures. The location of the karst system can be accurately determined through seismic and rock core calibration, and then an identification template for karst structural units in the area of interest can be established through fine interpretation of the imaging logging result. Such an approach has advantages of high precision and accuracy for analyzing the vertical structural characteristics of a karst system.

[0059] Please see Fig. 13. A well Zhonggu 111 is located on the paleokarst slope in the west of Tazhong North Slope, and the total thickness of Yingshan Formation encountered in the drilling is 168m.

[0060] Dividing the karst structure of a single well: the vertical composition of the karst structure of a single well is divided, according to the type of each karst structural unit determined with the above scheme and the characteristics of the corresponding imaging logging image;

[0061] On the basis of the division of the karst structure of each single well, profiles of successive wells in multiple directions are selected in the area of interest, the karst structural units are compared among the successive wells, the karst evolution stages are divided, and, in conjunction with seismic-well comparison, the karst development section of the target interval is determined to be at 6,080m to 6,180m, and is represented by a typical bead-like reflection feature in the seismic profile.

[0062] According to the division of the karst structural units, the section at 6,080m to 6,093m is a surface karst section, where the natural gamma value at the top interface of Yingshan Formation suddenly rises, and the imaging logging image shows alternative light and dark thin layers, representing a large-size superficial karst cave filled by mud. The section at 6,093m to 6,182m is a section with densely developed sections of fractures and dissolution pores, and a cave in about 4m height filled by mud exists at 6,093m. The imaging logging image shows disordered dark spot-shaped dissolution pores and dark dissolution fractures in a mesh structure, and the fractures and dissolution pores are developed more densely near the surface karst zone. At 6,102.5m and 6,104m near the surface karst zone, there are developed mud-filled caves in about 0.5m height. In the section from 6,182m to the bottom of the well, the imaging logging image shows bright yellow blocks or strips, and the natural gamma curve is relatively straight, representing is a high-resistance interval.

[0063] The vertical combination characteristics of karst structural units of a single well are divided. From top to bottom, the sections are surface mud-filled karst cave - low-angle fracture and small karst cave section - high-angle fracture and dense dissolution pore section.

[0064] The karst structures are compared among the successive wells. Through cross-comparison of karst cave layers, three sets of karst cave layers that are comparable among the successive wells are identified in the target interval. Among them, the karst cave layer at 40m to 100m from the top surface of Yingshan Formation is widely distributed. The formation mechanism of karst structures is further discussed on the basis of the comparison of profiles of the karst structures.

[0065] In the end, it should be noted: the embodiments described above are only some preferred embodiments of the present invention, and should not be deemed as constituting any limitation to the present invention. Though the present invention is described and illustrated in detail with reference to the embodiments, those skilled in the art can easily make modifications to the technical scheme described above in the embodiments or make equivalent replacement of some technical features. Any modification, equivalent replacement, or improvement made to the embodiments without departing from the spirit and the principle of the present invention shall be deemed as falling into the scope of protection of the present invention..

Claims

Claims 1. A method for identifying deep carbonate karst structures, comprising the following steps: Si: identifying a karst development site in a target interval of a well point in a target area; S2: capturing and interpreting an imaging logging image to determine the specific depth range of karst structure development in the target interval; S3: identifying the characteristics of each constituent unit of the karst structure, including characteristics of the rock core, characteristics of the imaging logging image, and response characteristics of a conventional logging curve.
2. The method for identifying deep carbonate karst structures according to claim 1, wherein the step S1 comprises: selecting a seismic profile at the well point in the target area, making well-seismic comparison, calibrating the level of the target interval, determining the position of the target interval on the seismic profile, and identifying the characteristics of the seismic reflection in-phase axis of the target interval, wherein a vertical "bead-like" reflection zone or a reflection zone with laterally enhanced amplitude indicates the development of karst caves.
3. The method for identifying deep carbonate karst structures according to claim 2, wherein the step S2 comprises: identifying the karst structure at the corresponding position on the imaging logging image according to the position of the target interval calibrated through well-seismic comparison in the step S1: specifically, interpreting the imaging logging image of the coring section of the target interval of a single well where a rock core is taken, and calibrating the rock structure and tectonic characteristics of the cores at the same depth with the imaging logging image, so as to obtain the accurate depth of the target structures as well as the presentation forms of various karst structures in the imaging logging image.
4. The method for identifying deep carbonate karst structures according to claim 3, wherein the step S3 comprises: using the characteristics of the imaging logging image and a conventional logging curve, especially a natural gamma curve, in combination to identify each karst structural unit.
5. The method for identifying deep carbonate karst structures according to claim 4, wherein the identifying method comprises: 1) identifying the filling characteristics in a karst cave, including the accumulation characteristics of filling materials, the arrangement pattern of filling breccia, and the relative mud content in the filling materials, according to the morphological and color characteristics of the imaging logging image of the karst cave development interval in conjunction with the natural gamma value; 2) for a well point without imaging logging image, identifying whether the formation is karst formation or non-karst formation through statistical analysis of conventional logging values: specifically, five sensitivity parameters, including lithologic density logging parameter, neutron porosity logging parameter, mudstone content logging parameter, shallow lateral conductivity, and absolute value of difference between bilateral conductivity, can be selected and used for pairwise cross-plotting, and determining whether the formation is karst formation or non-karst formation according to the distribution range of the logging values on the cross plot diagram.
6. A method for describing deep carbonate karst structures, comprising the following steps: Si: dividing the karst structure of a single well: dividing the vertical composition of the karst structure of a single well, according to the type of each karst structural unit determined with the above scheme and the characteristics of the corresponding imaging logging image; S2: comparing the karst profiles of successive wells: selecting the profiles of successive wells in multiple directions in the area of interest, comparing the karst structural units among the successive wells and dividing karst evolution stages, on the basis of the division of the karst structure of each single well; S3: with reference to the characteristics of the imaging logging images of the karst units obtained in step 2, selecting a typical single well, interpreting the imaging logging image of the karst development intervals of the single well, and analyzing the combination forms, distribution types and characteristics of the karst structural units in the vertical direction; S4: after analyzing the characteristics of the karst structural units of typical single wells in the vertical direction, selecting the profiles of multiple successive wells in transverse and longitudinal directions in the target area and carrying out cross-comparison, and analyzing the evolution stages of the karst structures with reference to the regional evolution characteristics of the target area.

CHINA UNIVERSITY OF GEOSCIENCES, BEIJING By its Patent Attorneys ARMOUR IP

P2374AU00