Method and device for predicting residual oil distribution of low-permeability reservoir
By combining core, well logging, and cast thin section data, and refining sedimentary characteristics to the lithofacies level, the problem of predicting the distribution of remaining oil in low-permeability reservoirs has been solved. This has enabled more accurate research on reservoir heterogeneity and prediction of remaining oil distribution, guiding adjustments in oilfield development.
Patent Information
- Application Number
- CN202410654587.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-05-24
- Publication Date
- 2025-11-25
AI Technical Summary
Existing technologies struggle to accurately predict the distribution of remaining oil in low-permeability reservoirs, particularly in the study of reservoir heterogeneity, which impacts oilfield development.
By combining core, well logging, and cast thin section data, sedimentary characteristics are refined to the lithofacies level, sedimentary microfacies are classified and physical properties are characterized, and sedimentary microfacies properties are corrected and the distribution of remaining oil is determined by using core sample interpretation, well logging curve matching, and cast thin section analysis.
It improves the accuracy of remaining oil prediction in low-permeability reservoirs, guides oilfield development adjustments, optimizes reservoir heterogeneity research, clarifies reservoir quality differences, and provides accurate predictions of remaining oil-rich areas.
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Figure CN121006987A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of oil and gas development of conglomerate reservoir, and particularly relates to a method and device for predicting the distribution of remaining oil in a low-permeability reservoir. BACKGROUND
[0002] Most of the low-permeability sandstone reservoirs in China have entered the medium-high water cut stage after long-term water injection development. In order to achieve the goal of stable production of low-permeability oilfields, the research on reservoir heterogeneity is an important difficulty for old oilfields to find remaining oil in the middle and late development stages. The understanding of reservoir heterogeneity often guides the further development plan design and development measure adjustment of oilfields, and therefore, the research on reservoir heterogeneity has become one of the core research contents for finding remaining oil.
[0003] Different scales of geological sedimentary characteristics and sedimentary heterogeneity affect the architecture, physical property and heterogeneity characteristics of the reservoir, and finally result in extremely uneven distribution of remaining oil in the reservoir, so it is very difficult to predict the distribution of remaining oil.
[0004] Therefore, it is urgent to establish a method and device for predicting the distribution of remaining oil in a low-permeability reservoir to solve the problem of predicting the distribution of remaining oil. SUMMARY
[0005] The present application provides a method and device for predicting the distribution of remaining oil in a low-permeability reservoir, which improves the accuracy of the prediction of remaining oil in a low-permeability reservoir based on the comprehensive use of geological constraints, core, logging and cast thin section data, and can meet the production needs and be used in production practice.
[0006] In a first aspect, the present application provides a method for predicting the distribution of remaining oil in a low-permeability reservoir, which comprises: interpreting core samples of single wells in a study area, and dividing facies and determining facies characteristics according to the interpretation results; matching the facies characteristics of the core samples with logging curves to determine the logging curve characteristics corresponding to the sedimentary microfacies; dividing the sedimentary microfacies according to the logging curve characteristics for single wells in the study area which do not contain core samples, and determining the sedimentary microfacies distribution characteristics of the study area; determining the physical property representation of the sedimentary microfacies according to the determined sedimentary microfacies distribution characteristics; correcting the physical property representation results of the sedimentary microfacies according to the cast thin section analysis results to determine the corrected physical property representation results of the sedimentary microfacies; and determining the distribution of remaining oil according to the corrected physical property representation results of the sedimentary microfacies.
[0007] In a second aspect, the embodiments of the present application further provide a device for predicting remaining oil distribution in a low-permeability reservoir, the device comprising: a memory and a processor; the memory is configured to store a program for predicting remaining oil distribution in a low-permeability reservoir, and the processor is configured to read and execute the program for predicting remaining oil distribution in a low-permeability reservoir, and execute the method of any one of the above embodiments.
[0008] In a third aspect, the embodiments of the present application further provide a computer-readable storage medium, wherein the computer-readable storage medium stores a data processing program, and the data processing program is executed by a processor to execute the method of predicting remaining oil distribution in a low-permeability reservoir according to any one of the above embodiments.
[0009] Compared with the related art, the present application provides a method and device for predicting remaining oil distribution in a low-permeability reservoir, the method comprising: interpreting core samples of single wells in a study area, and dividing facies according to the interpretation results to determine facies characteristics; matching the facies characteristics determined by the core samples with logging curves to determine logging curve characteristics corresponding to sedimentary microfacies; for single wells in the study area that do not contain core samples, dividing sedimentary microfacies according to the logging curve characteristics, and determining sedimentary microfacies distribution characteristics of the study area; determining physical property characterization of the sedimentary microfacies according to the determined sedimentary microfacies distribution characteristics; correcting the physical property characterization results of the sedimentary microfacies according to the results of cast thin section analysis to determine corrected physical property characterization results of the sedimentary microfacies; and determining distribution of remaining oil according to the corrected physical property characterization results of the sedimentary microfacies. The present application improves the accuracy of remaining oil prediction in a low-permeability reservoir based on geological constraints, comprehensive use of core, logging and cast thin section data, and can meet the production needs for use in production practice.
[0010] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent from the description, or can be learned by practice of the present application. Other advantages of the present application can be realized and obtained by means of the instrumentalities and combinations pointed out in the following description. BRIEF DESCRIPTION OF DRAWINGS
[0011] The accompanying drawings are included to provide an understanding of the present application, and constitute a part of the specification, together with the embodiments of the present application, to explain the technical scheme of the present application, and do not constitute a limitation on the technical scheme of the present application.
[0012] Figure 1 The method flowchart for predicting remaining oil distribution in a low-permeability reservoir according to the embodiments of the present application;
[0013] Figure 2 The device schematic diagram for predicting remaining oil distribution in a low-permeability reservoir according to the embodiments of the present application;
[0014] Figure 3Flow chart of the method for predicting the distribution of remaining oil in low-permeability reservoirs in some example embodiments
[0015] Figure 4 Schematic diagram of core photograph analysis in some example embodiments
[0016] Figure 5 Schematic diagram of well log curve feature analysis in some example embodiments
[0017] Figure 6 Schematic diagram of sedimentary model of low-permeability reservoirs in shallow delta in some example embodiments
[0018] Figure 7 Schematic diagram of reservoir casting thin section analysis in some example embodiments
[0019] Figure 8 Schematic diagram of reservoir profile development prediction in some example embodiments
[0020] Figure 9 Schematic diagram of reservoir sedimentary heterogeneity prediction results in some example embodiments
[0021] Figure 10 Pie chart of perforation information statistics in some example embodiments DETAILED DESCRIPTION
[0022] A number of embodiments are described herein, but it should be understood that the descriptions are illustrative only and are not intended to limit the scope of the application. Numerous variations and modifications will become apparent to those skilled in the art once the nature of the application is understood. Descriptions are given of the preferred embodiments, and combinations of parts of the described embodiments, in relation to the several drawings. Descriptions of features in the drawings are typically given individually, but combinations of two or more features from different drawings can also be possible. Unless specifically intended to be limited, any feature described in relation to one embodiment can be combined with any other feature described in relation to any other embodiment. Any feature or element in any of the claims can be used in combination with any feature or element in any other claim.
[0023] This application includes and contemplates combinations of features and elements known to those skilled in the art. The embodiments, features, and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive scheme as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive schemes to form another unique inventive scheme as defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this application may be implemented individually or in any suitable combination. Therefore, the embodiments are not limited except by the limitations imposed by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.
[0024] Furthermore, in describing representative embodiments, the specification may have presented methods and / or processes as a specific sequence of steps. However, the method or process should not be limited to the specific order of steps described herein, to the extent that it does not depend on such a specific order. As will be understood by those skilled in the art, other sequences of steps are also possible. Therefore, the specific order of steps set forth in the specification should not be construed as a limitation of the claims. Moreover, the claims concerning the method and / or process should not be limited to the steps performed in the written order, and those skilled in the art will readily understand that these orders can be varied and still remain within the spirit and scope of the embodiments of this application.
[0025] Predicting the distribution of remaining oil in an oil reservoir is a comprehensive analytical method, often based on the logging response characteristics of development wells and integrating dynamic data from actual oilfield development. Extensive oilfield development experience demonstrates that during long-term oilfield development, timely reservoir understanding and research into oil-water movement patterns and remaining oil distribution are crucial for providing a basis for further development adjustments. Currently, domestic and international scholars have conducted extensive research on reservoir heterogeneity, oil-water movement patterns, and remaining oil distribution prediction in low-permeability oilfields, achieving certain results. This method primarily includes two aspects: core analysis and petrophysical facies analysis.
[0026] I. Core Analysis Method
[0027] Core analysis is the only method in residual oil prediction technology that can quantitatively determine residual oil saturation. Through extensive core observation and lithofacies-logging comparisons, high-quality reservoirs are selected for comparative prediction. Currently, the most commonly used method in China for determining and monitoring residual oil saturation is closed-loop coring. Other core analysis methods for residual oil include conventional coring, rubber-sheathed coring, wire mesh coring, pressure-sealed coring, and sponge coring.
[0028] Core analysis techniques are costly and difficult to obtain cores in certain areas. Furthermore, in low-permeability reservoirs, a combination of macroscopic and microscopic analysis is often required. Therefore, core analysis alone is not widely applicable for predicting remaining oil in low-permeability reservoirs.
[0029] II. Rock Physical Phase Method
[0030] The petrophysical facies method is a comprehensive reflection of various geological processes, representing genetic units formed under sedimentary, diagenetic, tectonic, and fluid alteration processes. Based on the relationship between planar permeability and remaining oil, and the relationship between the radius of major flow pore throats and remaining oil, the petrophysical facies analysis method applies geostatistical methods to divide the study area into multiple levels of petrophysical facies. It studies the controlling role of different petrophysical facies in the formation and distribution of remaining oil, thereby determining the petrophysical facies regions where remaining oil is distributed, and ultimately achieving the goal of remaining oil prediction.
[0031] The classification of petrophysical facies is mostly accomplished under different sedimentary and diagenetic models, and the prediction results are often inconsistent. The focus is on classification and statistics, which is often limited by insufficient data and a lack of analytical data for the study area. Therefore, it is difficult to apply it extensively in predicting remaining oil in low-permeability reservoirs.
[0032] The inventors of this application have discovered that, in low-permeability reservoirs, the reservoir quality controlled by sedimentary heterogeneity has always been an important basis for proving potential oil and gas reserves and developing oil and gas reservoirs. Further research, description, and characterization are needed of the sedimentary characteristics (including lithofacies analysis, microfacies analysis, microfacies distribution, and sedimentary environment) required for development in low-permeability reservoirs.
[0033] Previous studies on the sedimentary characteristics of low-permeability oil and gas reservoirs have often roughly divided the reservoir into underwater distributary channels and mouth bars. This invention, however, utilizes core, wireline logging, and cast thin section data to comprehensively analyze sedimentary characteristics and reservoir heterogeneity, further refining the research scale of sedimentary characteristics to the lithofacies level (lithofacies are the smallest sedimentary level observable by the naked eye; different lithofacies may form within the same sedimentary microfacies). This subdivides various sedimentary microfacies, clarifies the reservoir quality differences caused by sedimentary heterogeneity during development, and clearly resolves the complex interactions and genetic relationships between the two. Understanding the sedimentary characteristics of low-permeability oil reservoirs and their control over reservoir heterogeneity can provide new insights for oil and gas field development and effectively guide adjustments to future development policies.
[0034] This invention provides a method for predicting the distribution of remaining oil in low-permeability reservoirs, such as... Figure 1 As shown, the method includes steps S100-S150:
[0035] S100: Interpret the core samples from single wells in the study area, classify the lithofacies based on the interpretation results, and determine the lithofacies characteristics;
[0036] S110: Match the lithofacies characteristics determined by the core sample with the logging curve to determine the logging curve characteristics corresponding to the sedimentary microfacies;
[0037] S120: For single wells in the study area that do not contain core samples, sedimentary microfacies are divided according to the logging curve characteristics, and the distribution characteristics of sedimentary microfacies in the study area are determined.
[0038] S130: Based on the determined distribution characteristics of sedimentary microfacies, determine the physical properties of the sedimentary microfacies;
[0039] S140: Based on the analysis results of the thin sections of the casting, the physical property characterization results of the sedimentary microphases are corrected, and the corrected physical property characterization results of the sedimentary microphases are determined.
[0040] S150: Determine the distribution of remaining oil based on the physical property characterization results of the modified sedimentary microphases.
[0041] In one exemplary embodiment, the lithofacies include: massive fine sandstone facies, normal rhythmic fine sandstone facies, parallel bedding fine sandstone facies, anti-rhythmic fine sandstone facies, trough cross-bedding fine sandstone facies, massive bedding siltstone facies, wavy cross-bedding siltstone facies, and climbing bedding siltstone facies.
[0042] Lithofacies characteristics include one or more of the following: lithological characteristics, hydrodynamic conditions, differences in vertical distribution, sedimentary environment, and sedimentary process.
[0043] The sedimentary features include one or more of the following: color, sedimentary structure, grain size, and rounding of the lithofacies as observed in the core.
[0044] The sedimentary microfacies include: underwater distributary channels, mouth bars, sheet sands, underwater natural dikes, and distal bars.
[0045] In one exemplary embodiment, matching the lithofacies characteristics determined from the core sample with the logging curve to determine the sedimentary microfacies logging curve characteristics and sedimentary characteristics of a single well includes:
[0046] The lithofacies characteristics of the core samples were correlated with the characteristics of anomalous points in the acoustic time difference in the logging curves according to depth.
[0047] Determine the lithofacies characteristics corresponding to different sedimentary microfacies;
[0048] Based on the correspondence between the lithofacies characteristics and the logging curves, the logging curve characteristics of each sedimentary microfacies are determined.
[0049] In one exemplary embodiment, determining the distribution characteristics of sedimentary microfacies in the study area includes:
[0050] Determine the sedimentary microfacies corresponding to each single well from bottom to top in the depth domain;
[0051] Establish a well profile of multiple wells to determine the sedimentary microfacies corresponding to reservoirs at different depths;
[0052] The distribution characteristics of sedimentary microfacies in the study area were determined based on the sedimentary microfacies from multiple wells.
[0053] In one exemplary embodiment, the physical property characterization results of sedimentary microfacies are verified based on the analysis results of the cast thin sections. Specifically, the verification of the cast thin sections is used to determine which of two sedimentary microfacies with similar physical properties in the quantitative results of the well logging curve has better physical properties at the microscopic level. Supporting evidence Figure 6 The conclusion that the microphase properties of the water-bound particles in the China-Vietnam region are worse is further up the vertical direction, with properties decreasing towards the surface. More detailed characterization of these properties corroborates the previous assessment.
[0054] In one exemplary embodiment, the distribution of remaining oil is determined based on the physical property characterization results of the modified sedimentary microfacies: after areas with good physical properties have been developed, a secondary area is selected, namely a relatively undeveloped location with slightly worse physical properties. The distribution of remaining oil here is equivalent to the distribution location of the sedimentary microfacies at the secondary level of physical properties.
[0055] The method for predicting the distribution of remaining oil in low-permeability reservoirs implemented in this embodiment has the following technical advantages:
[0056] By utilizing core, wireline logging, and cast thin section data, a comprehensive analysis of sedimentary characteristics and reservoir heterogeneity is conducted, further refining the study scale of sedimentary characteristics down to the lithofacies level. This allows for the subdivision of various sedimentary microfacies, clarifying the reservoir quality differences caused by sedimentary heterogeneity during development, and clearly resolving the complex interactions and genetic relationships between the two. Through the study of sedimentary heterogeneity, the distribution of reservoir heterogeneity is reflected, achieving the goal of predicting remaining oil.
[0057] Secondly, embodiments of the present invention also provide an apparatus for predicting the distribution of remaining oil in low-permeability reservoirs, such as... Figure 2 As shown, the apparatus includes a memory 200 and a processor 210; the memory is used to store a program for predicting the distribution of remaining oil in low-permeability reservoirs, and the processor is used to read and execute the program for predicting the distribution of remaining oil in low-permeability reservoirs, and to execute the method described in any of the above embodiments.
[0058] Thirdly, embodiments of the present invention also provide a computer-readable storage medium storing a data processing program, wherein the data processing program is executed by a processor using the method for predicting the distribution of remaining oil in a low-permeability reservoir as described in any of the above embodiments.
[0059] Example 1
[0060] This example uses a method for predicting the distribution of remaining oil in low-permeability reservoirs to study a low-permeability oilfield in the Ordos Basin. The specific process is as follows: Figure 3 As shown, the steps are as follows:
[0061] Step 1: Based on the core data, the lithofacies were divided and the lithofacies characteristics were analyzed.
[0062] The sandstone in the study area is typical of shallow-water deltaic sediments, and the lithofacies assemblage of the work area was analyzed. Core description and interpretation revealed the lithofacies characteristics of the Chang 6 section of Wuliwan. Based on differences in lithology and vertical distribution, the lithofacies can be divided into eight categories: massive fine sandstone facies, normal rhythmic fine sandstone facies, parallel bedding fine sandstone facies, anti-rhythmic fine sandstone facies, trough cross-bedding fine sandstone facies, massive bedding siltstone facies, wavy cross-bedding siltstone facies, and climbing bedding siltstone facies.
[0063] Figure 4 This is a schematic diagram of core photograph analysis according to a specific embodiment of the present invention. It details the characteristics of each lithofacies from the perspectives of lithological features, hydrodynamic conditions, sedimentary environment, and sedimentary process.
[0064] Taking the massive fine sandstone facies as an example, the massive fine sandstone facies of Wuliwan are generally brown, tan, gray, or grayish-white, with little mud content. The sandstone is fine-grained and well-sorted, forming a homogeneous rhythmic cycle, usually formed by the superposition of several cycles. Its depositional thickness is large, ranging from 0.8 to 4 meters, with the bottom of individual cycles often showing an eroded basement. This facies varies in thickness and typically appears as unstructured layered sandstone, occasionally with tuffaceous sandstone. Due to the high quartz content of the parent rock, the clastic particles produced by mechanical weathering were transported to the delta front and deposited, resulting in the inherited gray and grayish-white colors of the massive sandstone facies shown in the core. Another portion of the brown and tan massive sandstone facies reflects a strong oxidizing environment during reservoir deposition, indicating a shallow water environment in the delta front depositional setting of the study area. The fine-grained sandstone, representing the coarsest sandstone facies in the study area, reflects the most intense and stable hydrodynamic characteristics at the delta front. At the bottom of the sandstone layer, objects carried by the water flow repeatedly impacted the substrate, forming a stable scour base. Therefore, the massive fine-grained sandstone can be interpreted as a pure, sandy underwater distributary channel. The occasionally observed tuffaceous sandstone reflects saline magmatic activity in the Chang 6 section, with the tuffaceous sandstone solidifying within the massive fine-grained sandstone.
[0065] Step 2: Based on the logging data, the sedimentary facies are divided, and the results of the core description are assigned to the logging curves at the same depth of the core well. This allows us to summarize the morphological characteristics of the lithofacies and sedimentary microfacies observed by the naked eye on the curves, and summarize the logging curve characteristics of 5 sedimentary microfacies.
[0066] In this step, the results of core observations are attributed to well logging data, which can be obtained using wireline logging data, such as gamma-ray (GR), acoustic propagation time (AC), and resistivity (RT), to assist in interpreting sedimentary facies. The purpose of gamma-ray logging analysis is to detect clean sections (containing more sand) and dirty sections (containing more clay). Considering that clayey elements have high gamma-ray values and sandy elements have low gamma-ray values, wireline logging often struggles to identify lithofacies bedding types; well logging primarily identifies lithology and struggles to identify structural features such as bedding color.
[0067] This step involves categorizing the results of core observations into well logging curves, summarizing the characteristics of five sedimentary microfacies well logging curves. This method identifies the lithofacies type of a single well and summarizes the response type of each sedimentary microfacies well logging curve.
[0068] Figure 5 This diagram illustrates the characteristics of well logging curves. The well logging curve response exhibits a high-amplitude box-shaped GR curve, with overall homogeneity, reflecting a stable sedimentary environment with abundant and rapid sediment accumulation under stable hydrodynamic conditions. This is generally interpreted as underwater distributary channel deposition. Smooth curves can be interpreted as massive fine sandstone facies, while curves with certain serrations can be considered as ascending bedding siltstone facies, representing underwater natural levee microfacies.
[0069] In cable logging responses, a trend characterized by a wider upper section, a narrower lower section, and an upward-pointing tail is known as a bell-shaped superposition pattern. This indicates that the hydrodynamic force of sediment deposition weakens from bottom to top, while the clay content increases in the upper part. Specifically, this manifests as an increase in gamma logging values, and after core repositioning, the sediment is mostly composed of fine sandstone with positive rhythmic bedding, which also indicates a subaqueous distributary channel microfacies, representing a channel in the middle or lacustrine region.
[0070] The funnel-shaped coarsening trend observed in the cable logging response indicates that the hydrodynamics of the sediments increased from bottom to top. With increased hydrodynamics, the upper sands became purer, and the gamma logging values and clay content gradually decreased. The logging response in the Chang 6 section exhibited this distinct anti-rhythmic depositional funnel-shaped coarsening trend. After core repositioning, the sediments revealed obvious anti-rhythmic bedding fine sandstone facies, trough-cross-bedding fine sandstone facies, or wavy-cross-bedding siltstone facies. Statistical classification showed that smooth curves mostly indicated near-mouth bar deposits of anti-rhythmic bedding fine sandstone facies; serrated curves indicated near-mouth bar microfacies of trough-cross-bedding; and serrated curves with relatively low amplitude indicated sheet-like sand microfacies.
[0071] The logging response shows an irregular trend, which usually occurs in thin layers of sandstone or interbedded mudstone and sandstone. In this case, the sedimentary hydrodynamics are weak or unstable, indicating that the sedimentary location is far from the shore and is affected by rivers, tides and waves. This is thus identified as a remote bar microfacies.
[0072] Step 3: Determine the development pattern and characteristics of sedimentary microfacies in the region.
[0073] like Figure 6 The shallow-water deltaic sedimentary low-permeability reservoir depositional model shows an underwater distributary channel microfacies. Previous studies have indicated that this area experienced deposition with limited space and abundant sediment supply. The main developments include vertically massive, lenticular, flat-topped-bottomed channels with large planar distribution. This type of microfacies is the most widespread and largest in scale in the study area. Due to the strong hydrodynamic characteristics of the depositional environment, underwater distributary channels often deposit thick, coarse-grained sediments. Therefore, core analysis shows that the underwater distributary channel sediments in the Changyi Formation, Section 6, are dominated by brownish-gray massive fine sand with low mud content. These are mostly rhythmically bedding fine sandstone facies or massively bedding fine sandstone facies, exhibiting the greatest thickness among the microfacies and representing the main sand body type of the delta front subfacies. Furthermore, due to strong and stable hydrodynamic scouring, parallel-bedding fine sandstone facies can also form. The underwater distributary channels in the core are often composed of combinations of these three facies.
[0074] The sedimentary characteristics of the Chang 6 section are summarized as follows: Figure 6 During the depositional period of the Chang 6 Member, the available space was limited, and underwater distributary channels were widely distributed along the delta front. Due to the lowering of the base level, the available space was small, but the sediment supply was abundant. Therefore, the delta was dominated by river action, representing a highly constructive deltaic depositional model. At the river inflow points, as the underwater distributary channels at the delta front gradually bifurcated and migrated, the sedimentary sand bodies also gradually expanded. During the depositional process, the channel sand bodies exhibited vertical superposition characteristics in the longitudinal direction, and laterally, they were mainly the superposition of underwater distributary channels and sandbars. The underwater distributary channels were mainly filled with fine sandstone, with a laterally thinning thickness. They are the most important and widely distributed sedimentary microfacies and high-quality reservoirs in the study area. However, due to the filling and clogging of pores by transported fine-grained sediments, the porosity and permeability were reduced, and the calcareous cementation during diagenesis resulted in ultra-low permeability reservoirs. The reduced available space during the Chang 6 Member period led to the slow accumulation and widespread superposition of underwater distributary channels in the plane, and the channel sand bodies in the profile exhibited a stacked pattern. The composite superposition of sand bodies of different origins led to the development of a highly heterogeneous reservoir in the Chang 6 section.
[0075] Step 4: Analyze the reservoir casting sections.
[0076] The study clarified the variation characteristics of reservoir properties, showing a deterioration in properties from bottom to top. The subaqueous distributary channel microfacies properties were stronger than those of the upper sheet sands and distal sandbars. In section 6, the lower subaqueous distributary channel properties were stronger than those of the mouth bar, while the subaqueous distributary channel in the middle section was similar to the mouth bar. In the upper reservoir, the mouth bar properties were stronger than the sheet sands, and the sheet sands were stronger than the distal sandbars. This corroborates geological understanding and refines the comparison of sedimentary microfacies properties in areas difficult to quantitatively describe using well logging curves.
[0077] Analysis suggests that the Chang 6 section sandstone is primarily composed of feldspar and quartz. Overall, the sandstone is mineralogically mature. The studied sandstone exhibits high vertical heterogeneity. Furthermore, the reservoir quality of the Chang 6 section is considered to be mainly controlled by the type and distribution of sedimentary microfacies. Core and wireline logging indicate that the fine sandstone with positive rhythmic characteristics can be interpreted as a subsea distributary channel, suggesting a gradual decrease in hydrodynamic force along the vertical sequence. Figure 7 The analysis of the reservoir casting sections shown reveals that the lower sequence minerals are well-sorted, exhibiting sub-angular to sub-rounded shapes, with directional mineral arrangement and approximately 99% residual intergranular porosity, reflecting relatively strong and stable hydrodynamics. In contrast, the upper sequence minerals are relatively poorly sorted, exhibiting angular to sub-angular shapes, with mica deformation, and approximately 90% residual intergranular porosity, along with some micropores. The uneven pore distribution indicates strong but unstable hydrodynamics. These differences in hydrodynamics lead to different sedimentary characteristics and control the heterogeneity of the reservoir.
[0078] Step 5: Based on the facies interpretation and analysis of conventional core, cable logging and extracted thin sections, the distribution of sedimentary microfacies was predicted.
[0079] Based on facies interpretation and analysis using conventional core logging, wireline logging, and extracted thin sections, the distribution of sedimentary microfacies was predicted. Identifying lateral and vertical facies changes provided a basis for recognizing reservoir geometry, sealing characteristics, sedimentary layers, and determining reservoir heterogeneity. Figure 8This is a reservoir profile development prediction map, showing the development characteristics of sand bodies or sedimentary microfacies in the vertical direction, providing model guidance for subsequent single-well and multi-well predictions. At a vertical sequence scale of approximately 150m covering the Chang 6 Member, the microfacies of the same depositional period are superimposed vertically according to the facies sequence law. The sedimentary layers in the study area clearly begin with the thick distributary channel deposits near the lower shore of the Chang 6 Member, with individual channel thicknesses ranging from 1 to 5m. The thickness of the underwater distributary channel sandstone is composed of superimposed sandstone from multiple channels, developed in a highly constructive deltaic front depositional environment with limited containment space, providing the basis and conditions for frequent channel distributary and migration. The central part of the Chang 6 Member is the central deltaic front, dominated by sediments of numerous superimposed underwater distributary channels and mouth bars. The thin sand bodies at the distal end are often located in the upper part of the Chang 6 Member near the prodelta, consisting of thick, vertically superimposed muddy deposits and rare thin sandstone layers.
[0080] Step 6: Reservoir Property Analysis
[0081] Different physical property curves in the logging curves corresponding to different sedimentary microfacies can be used to read an average value of porosity or permeability based on thickness. In the identification of sedimentary microfacies from core wells to other wells, the reservoir distribution in the study area can be clearly identified.
[0082] Based on the above methods, well logging interpretation and physical property analysis were conducted on 86 wells in the entire area. The statistical results show that the Chang 6 section mainly consists of underwater distributary channels and mouth bars. Due to the strong hydrodynamics in the depositional environment of these two types of microfacies, the sediments are well sorted and have coarse grain size. Therefore, the physical properties of these two microfacies are relatively good. The average porosity of the underwater distributary channels is 11.42%, and the average permeability is 2.74 mD; the average porosity of the mouth bars is 11.57%, and the average permeability is 2.41 mD; the sheet sand deposits are of relatively moderate thickness, and the sediments are mostly good siltstone, with an average porosity of 10.53% and an average permeability of 1.55 mD; the underwater natural dikes are mostly developed on the side of the channels, with coarse grain size and thin thickness, and fewer sample points. The average porosity of these dikes is 10.34%, and the average permeability is 1.34 mD; the average porosity of the distal dikes is 9.90%, and the average permeability is 0.99 mD. The differences in sedimentary environments of each sedimentary microfacies lead to variations in their physical properties. Furthermore, the complex superposition relationships between different microfacies, or even within the same microfacies, result in strong vertical and horizontal heterogeneity in the reservoirs of the study area. Statistical analysis indicates that the underwater distributary channels and mouth bars exhibit the best physical properties, followed by sheet-like sand microfacies and underwater natural levee microfacies, with distal sandbars exhibiting the worst properties. Figure 9 This is a specific embodiment of the present invention, showing the prediction results of reservoir sedimentary heterogeneity.
[0083] Step 7: Compile perforation information
[0084] The location of the perforation is the vertical development position of the oil well. Collecting this information helps to identify the developed sedimentary microfacies or reservoir locations, summarize the potential for further development, and determine where perforations can be made. Summarizing the prediction results on a macroscopic scale is a step in remaining oil prediction.
[0085] Figure 10 As a specific embodiment of the present invention, a pie chart of perforation information statistics is presented. We found that among the three oil groups in the Chang 6 formation, the Chang 62 reservoir is the most developed. Furthermore, the reservoir quality of both Chang 63 and Chang 62 is superior to that of Chang 61. After systematic research using the above method, it was found that in the perforation results of 86 single wells in the experimental area, the perforated intervals of the development wells were basically located in the main oil layer, Chang 62. The perforated sedimentary microfacies include subsea distributary channels, mouth bars, and sheet sands. The results indicate that initial reservoir development work mainly focused on the subsea distributary channel microfacies with the best physical properties. When the current development enters a high water-cut stage, the next development direction should focus on the non-major microfacies of Chang 62, such as the mouth bars and sheet sands with slightly poorer physical properties. These microfacies types of reservoirs should be the remaining oil-rich areas in the later stages of development of the Wuliwan low-permeability oil reservoir. In addition, within the oil group range, the results show that the reservoir development quality of Chang 63 is slightly better than that of Chang 61. Its development level is relatively low and it can be used as an alternative development layer for Chang 62. Therefore, it can be predicted that the remaining oil-rich areas of the Wuliwan low-permeability reservoir are located in the mouth bar and sheet-like sand microfacies of the Chang 62 section, as well as in the Chang 63 section reservoir. These reservoirs will be the focus of the next stage of oilfield development, including preparation for perforation methods and well location deployment.
[0086] Step 8: Areas where residual oil is concentrated
[0087] The conclusion that reservoir heterogeneity is controlled by sedimentary microfacies is established. By identifying these microfacies on the well surface, areas with slightly poorer physical properties are identified. These relatively poor-property-valued microfacies are precisely the regions enriched with remaining oil. The final interpretation results provide a basis for subsequent development adjustments. For example, in water injection development, water flow often displaces crude oil along reservoirs with good physical properties. Therefore, through core description, well logging characteristic summarization, multi-well sedimentary microfacies identification, sedimentary microfacies property analysis, and thin-section casting verification, the location of reservoirs with relatively poor physical properties, without perforation development, can be identified. Such reservoirs can be considered areas of remaining oil accumulation.
[0088] This invention utilizes the method of the present invention to study the development of a low-permeability oilfield in an oilfield in the Ordos Basin, analyzes the distribution of reservoir sand bodies and residual oil, and establishes a method for predicting residual oil in low-permeability reservoirs. This paper uses core data, wireline logging, and cast thin sections to study the reservoir sedimentary characteristics in the study area to address this problem. The results show that conventional core analysis indicates the Chang 6 Member reservoir is deposited at the delta front, and eight lithofacies can be identified based on sedimentary grain size and sedimentary structures. Five sedimentary microfacies can be further identified through different combinations of lithofacies. The reservoir properties of ultra-low permeability sandstone reservoirs are controlled by different sedimentary microfacies. Logging analysis shows that the sedimentary microfacies are mainly characterized by the best properties of underwater distributary channels and mouth bars, followed by sheet-like sand microfacies, underwater natural dike microfacies, and distal sand bars. Macroscopically, the lower and middle sand bodies of the Chang 6 Member have good connectivity, while the upper sand bodies have poor connectivity. Microscopically, sand body type and sedimentary microfacies distribution play a crucial role in controlling reservoir porosity and permeability. Understanding and studying reservoir sedimentary heterogeneity helps in better predicting and assessing reservoir quality and remaining oil distribution. This study will provide a basis for the development, recovery, and production capacity building of reservoirs with similar sedimentary environments and characteristics.
[0089] The final conclusion was that reservoir heterogeneity was controlled by sedimentary microfacies. By identifying these microfacies on the well surface, areas with slightly poorer physical properties were identified. These relatively poor-performing microfacies are precisely the regions enriched with remaining oil. The final interpretation results provide a basis for further development adjustments.
[0090] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.
Claims
1. A method for predicting the distribution of remaining oil in low-permeability reservoirs, characterized in that, The method includes: Core samples from individual wells in the study area were interpreted, and lithofacies were classified and lithofacies characteristics were determined based on the interpretation results. The lithofacies characteristics determined by the core samples are matched with the logging curves, and then the logging curve characteristics corresponding to the sedimentary microfacies are determined based on the relationship between the lithofacies characteristics and the sedimentary microfacies. For single wells in the study area that do not contain core samples, sedimentary microfacies are divided based on the logging curve characteristics, and the distribution characteristics of sedimentary microfacies in the study area are determined. Based on the determined distribution characteristics of sedimentary microfacies, the physical properties of the sedimentary microfacies are characterized. The distribution of remaining oil was determined based on the physical property characterization results of the sedimentary microfacies.
2. The method for predicting the distribution of remaining oil in low-permeability reservoirs according to claim 1, characterized in that, The lithofacies include: massive fine sandstone facies, normal rhythmic fine sandstone facies, parallel bedding fine sandstone facies, anti-rhythmic fine sandstone facies, trough cross-bedding fine sandstone facies, massive bedding siltstone facies, wavy cross-bedding siltstone facies, and climbing bedding siltstone facies.
3. The method for predicting the distribution of remaining oil in low-permeability reservoirs according to claim 1, characterized in that, The lithofacies characteristics include one or more of the following: lithological characteristics, hydrodynamic conditions, differences in vertical distribution, sedimentary environment, and sedimentary process.
4. The method for predicting the distribution of remaining oil in low-permeability reservoirs according to claim 1, characterized in that, The sedimentary features include one or more of the following: color, sedimentary structure, grain size, and rounding of the lithofacies as observed in the core.
5. The method for predicting the distribution of remaining oil in low-permeability reservoirs according to claim 1, characterized in that, The sedimentary microfacies include: underwater distributary channels, mouth bars, sheet sands, underwater natural dikes, and distal bars.
6. The method for predicting the distribution of remaining oil in low-permeability reservoirs according to claim 4, characterized in that, The process of matching the lithofacies characteristics determined from the core samples with the logging curves to determine the sedimentary microfacies logging curve characteristics and sedimentary characteristics of a single well includes: The lithofacies characteristics of the core samples were correlated with the characteristics of anomalous points in the acoustic time difference in the logging curves according to depth. Determine the lithofacies characteristics corresponding to different sedimentary microfacies; Based on the correspondence between the lithofacies characteristics and the logging curves, the logging curve characteristics of each sedimentary microfacies are determined.
7. The method for predicting the distribution of remaining oil in low-permeability reservoirs according to claim 4, characterized in that, The characteristics of the sedimentary microfacies distribution in the study area were determined as follows: Determine the sedimentary microfacies corresponding to each single well from bottom to top in the depth domain; Establish a well profile of multiple wells to determine the sedimentary microfacies corresponding to reservoirs at different depths; The distribution characteristics of sedimentary microfacies in the study area were determined based on the sedimentary microfacies from multiple wells.
8. The method for predicting the distribution of remaining oil in low-permeability reservoirs according to claim 1, characterized in that, Based on the determined distribution characteristics of sedimentary microfacies, after determining the physical properties of the sedimentary microfacies, the method also includes: The physical property characterization results of the deposited microphases were verified based on the analysis results of the thin sections of the casting.
9. An apparatus for predicting the distribution of remaining oil in low-permeability reservoirs, characterized in that, The apparatus includes a memory and a processor; the memory is used to store a program for predicting the distribution of remaining oil in low-permeability reservoirs, and the processor is used to read and execute the program for predicting the distribution of remaining oil in low-permeability reservoirs, and to execute the method according to any one of claims 1-8.
10. A computer-readable storage medium having a data processing program stored thereon, the data processing program being executed by a processor according to any one of claims 1-8, the method for predicting the distribution of remaining oil in a low-permeability reservoir.