Multi-information constraint complex lithology interpretation method and device

Abstract

The invention discloses a multi-information constraint complex lithology interpretation method and device. The method comprises the following steps: establishing a symbiotic relationship among various lithologies to be explained according to coring data, logging data and logging data in a research area based on the orderliness of vertical superposition and spatial distribution of sedimentary strata; on the basis of the current symbiotic relationship, the logging data is corrected, an intersection chart between the lithology and the sensitive logging curve is drawn, and the intersection chart is used for being combined with the symbiotic relationship to achieve lithology interpretation in the research area. According to the method, multiple information is integrated, multiple to-be-explained symbiotic relationships among lithology are used as constraints, and logging data are reasonably corrected, so that the prediction precision of the rendezvous chart between the lithology and the sensitive logging curve is higher; the method can be used for lithology interpretation of complex lithology, such as sedimentary rock, intrusive rock and intrusive rock overlying and underlying metamorphic rock co-development areas.

Description

Technical Field

[0001] This invention relates to the field of petroleum geophysical exploration technology, and in particular to a method and apparatus for interpreting complex lithology with multiple information constraints. Background Technology

[0002] Currently, the interpretation and identification of complex lithology mainly includes qualitative interpretation based on logging cuttings, qualitative interpretation based on logging response characteristics, chart solution based on logging response equations, equation system solution based on logging response equations, and statistical analysis based on core calibration logging, etc. (Zhao Xianling et al.)

[0003] (2015). With the continuous extension of the depth and breadth of oil and gas exploration, the lithology of reservoirs has become more complex. For example, weathering crusts and inner layers of buried hills from different eras, deep volcanic rocks and intrusive contact metamorphic rocks, lake basin central carbonate rocks, and oil shale have become important targets for deep oil and gas exploration in major onshore oil and gas basins. These complex lithological reservoirs are characterized by their large burial depth, poor reservoir properties, complex and variable lithology, and small differences in well logging responses. Among them, intrusive contact metamorphic rocks are a particularly special type of complex lithological target. They are shallowly metamorphosed complex lithologies formed by the intrusion of high-temperature magma into clastic surrounding rocks, leading to contact metamorphism of the adjacent surrounding rocks. Interpreting these "transitional" lithologies between metamorphic and clastic rocks is often difficult. Due to the influence of high-temperature hydrothermal fluids, cuttings logging results in lithological names such as argillaceous dolomite, dolomitic limestone, carbonate rocks, andesite, rhyolite, and tuff. For example, in the igneous rock section of the xx Well Sha-3 in the xx Depression, the cuttings logging named the thick layer of diabase as basalt, and the overlying and underlying contact metamorphic rocks as andesite and rhyolite. This resulted in the simultaneous appearance of volcanic rocks of different types of magma in the Cenozoic strata, leading to extremely chaotic lithological interpretation and unclear understanding of the development law of high-quality reservoirs. Summary of the Invention

[0004] The inventors discovered several shortcomings in existing methods for interpreting complex lithology. First, core sampling is the most direct technical means of interpreting complex lithology, allowing for accurate identification of lithology through visual observation. However, due to high costs, the number of core wells and core sections within the same zone is relatively small, resulting in very limited core observation and analysis data for typical lithologies. Second, cuttings logging is a more intuitive method for reflecting lithology, allowing geologists to obtain the lithology of the entire well section through detailed cuttings descriptions. Under current drilling technology, cuttings logging sampling intervals are 2 meters, and cuttings are mostly powdery, leading to significant differences in descriptions by different geologists, often making it difficult to accurately identify and describe complex lithology (Zhang Yuxin et al., 2021). Finally, existing logging series contain not only lithological information but also formation fluid responses, resulting in a degree of ambiguity in the interpretation of complex lithology through logging. Meanwhile, existing well logging interpretation methods for complex lithology include curve reconstruction, eigenvalue lithology identification charts, and neutron density envelope models. These methods are all based on forward and inverse modeling theory, cross plots, statistics, or intelligent algorithms such as convolutional neural networks and support vector machines. They utilize well logging data to calculate various parameters or construct different models to identify complex lithology (Guo Xusheng, 2018; Liu Mi, 2020; Tang Ziyu, 2020; Yang Shihe, 2021). However, for the interpretation of complex lithology, the principles, procedures, parameter selection, and experience requirements of the interpreters of the above methods all differ to some extent.

[0005] In order to at least partially solve the technical problems existing in the prior art, the inventors made this invention, which provides a method and apparatus for interpreting complex lithology with multiple information constraints through specific implementation methods. This method can integrate multiple information and use the symbiotic relationship between intrusive rocks and metamorphic rocks as constraints to achieve accurate interpretation of complex lithology.

[0006] In a first aspect, embodiments of the present invention provide a method for interpreting complex lithology with multiple information constraints, including:

[0007] Based on the ordered vertical stacking and spatial distribution of sedimentary strata, and using core data, well logging data, and well logging data from the study area, we established symbiotic relationships among various lithologies to be interpreted.

[0008] Based on the current symbiotic relationship, the logging data is corrected, and a cross plot is drawn between lithology and sensitive logging curves. The cross plot is used in conjunction with the symbiotic relationship to realize the lithological interpretation in the study area.

[0009] Secondly, embodiments of the present invention provide a complex lithology interpretation device with multiple information constraints, comprising:

[0010] The symbiotic relationship establishment module is used to establish symbiotic relationships between various lithologies to be interpreted based on the orderliness of the vertical stacking and spatial distribution of sedimentary strata, and according to core data, well logging data and well logging data in the study area.

[0011] The cross-plot drawing module is used to correct logging data based on the current symbiotic relationship and draw cross-plots between lithology and sensitive logging curves. The cross-plots are used in conjunction with the symbiotic relationship to realize lithological interpretation in the study area.

[0012] Thirdly, embodiments of the present invention provide a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the aforementioned complex lithology interpretation method with multiple information constraints.

[0013] Fourthly, embodiments of this disclosure provide a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the aforementioned complex lithology interpretation method with multiple information constraints.

[0014] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0015] (1) The complex lithology interpretation method with multiple information constraints provided in this embodiment of the invention integrates coring data, logging data, and well logging data to establish a lithology columnar section, obtaining the symbiotic relationships between various lithologies to be interpreted; based on the current symbiotic relationships, the well logging data is corrected, and a cross-plot between lithology and sensitive logging curves is drawn. This cross-plot, constrained by the symbiotic relationships between various lithologies to be interpreted, can improve the accuracy and precision of complex lithology interpretation in the study area, supporting efficient exploration and profitable development of deep, complex, and tight reservoir oil and gas reservoirs.

[0016] (2) The complex lithology interpretation method with multiple information constraints provided in this embodiment of the invention is based on a multi-mineral model and constructs a first lithology coding curve through an optimized well logging interpretation method. With the first lithology coding curve as a reference, the symbiotic relationship between multiple lithologies to be interpreted and the complex lithology interpretation cross plot are iteratively optimized to further improve the accuracy and precision of complex lithology interpretation.

[0017] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0018] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0020] Figure 1 This is a flowchart of the complex lithology interpretation method with multiple information constraints in Embodiment 1 of the present invention;

[0021] Figure 2 for Figure 1 The detailed implementation flowchart of step S11 is shown below;

[0022] Figure 3 This is a flowchart of the complex lithological interpretation method with multiple information constraints in Embodiment 2 of the present invention;

[0023] Figure 4 This is an example diagram illustrating the construction of complex lithological symbiotic relationships in Embodiment 2 of the present invention;

[0024] Figure 5 This is an example diagram of a complex lithological intersection chart in Embodiment 2 of the present invention;

[0025] Figure 6 This is an example graph showing the calculation results of the percentage of mineral components, porosity, and saturation curves in complex lithology in Embodiment 2 of the present invention;

[0026] Figure 7 This is a schematic diagram of the complex lithology interpretation device with multiple information constraints in an embodiment of the present invention. Detailed Implementation

[0027] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0028] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Every smaller range between any stated value or intermediate value within a stated range, and any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0029] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0030] Example 1

[0031] Embodiment 1 of the present invention provides a method for interpreting complex lithology with multiple information constraints, the process of which is as follows: Figure 1 As shown, it includes the following steps:

[0032] Step S11: Based on the vertical stacking and spatial distribution order of sedimentary strata, establish the symbiotic relationships among various lithologies to be interpreted according to the core data, well logging data and well logging data in the study area.

[0033] Taking a region where sedimentary rocks, intrusive rocks, and overlying and underlying metamorphic rocks coexist as an example, establishing symbiotic relationships among various unexplained lithologies can be achieved, such as establishing symbiotic relationships between intrusive rocks and metamorphic rocks. In this case, the vertical stacking and spatial distribution order of sedimentary strata mainly includes the genetic development law of contact metamorphic rocks of a certain thickness formed at the top and bottom of the contact surface by the thermal baking and hydrothermal action of igneous rocks intruding into the sedimentary host rocks.

[0034] Specifically, regarding the establishment of the symbiotic relationship between intrusive and metamorphic rocks, see [link to relevant documentation]. Figure 2 As shown, it includes the following steps:

[0035] Step S111: Based on the coring data, logging data and well logging data in the study area, obtain the target layer lithology columnar section of multiple wells.

[0036] Based on the lithological description data from the core data in the study area, the conventional logging and imaging logging data from the well logging data, and the sedimentary facies distribution data and well test data of the target layer in the study area, the mapping relationship between the logging curve response characteristics of different lithologies in the core section and the imaging logging images, logging lithology naming, oil and gas shows and gas measurement values ​​was analyzed, and target layer lithology columnar sections of multiple wells were obtained.

[0037] Step S112: Determine the thickness of the intrusive rock and the total thickness of the metamorphic rocks above and below the intrusive rock from the lithological columnar section of the target layer of each well. Determine the quality factor of the intrusive rock based on the density, velocity and thickness of the intrusive rock, and obtain a sample data containing the quality factor of the intrusive rock and the total thickness of the metamorphic rocks to establish a sample set.

[0038] Based on the density, velocity, and thickness of the intrusive rock, the intrusive rock quality factor is determined using the following formula (1):

[0039] TP=Log(ρ1×v1×h1)(1)

[0040] In equation (1), TP is the intrusive rock quality factor, ρ1 is the intrusive rock density, v1 is the intrusive rock velocity, and h1 is the intrusive rock thickness.

[0041] Step S113: Based on the sample set, obtain the correspondence between the intrusive rock quality factor and the total thickness of the metamorphic rock through fitting method, and characterize the symbiotic relationship between intrusive rocks and metamorphic rocks.

[0042] Through fitting, if the intrusive rock quality factor is greater than a certain value, the corresponding relationship between the intrusive rock quality factor and the total thickness of the metamorphic rock is as follows:

[0043] h2=a×TP-b(2)

[0044] In equation (2), h2 is the total thickness of the metamorphic rock, TP is the quality factor of the intrusive rock, and a and b are constants determined by fitting.

[0045] Step S12: Based on the current symbiotic relationship, correct the logging data and draw a cross plot between lithology and sensitive logging curves. The cross plot is used in conjunction with the symbiotic relationship to realize the lithological interpretation in the study area.

[0046] After obtaining the corrected logging data, a logging lithology coding curve is constructed. Based on this, a sensitive logging curve for complex lithology identification is selected, and the selected logging curves are used for cross-plotting and porosity-permeability interpretation to draw a complex lithology cross-plot.

[0047] The complex lithology interpretation method with multiple information constraints provided in Embodiment 1 of this invention integrates coring data, well logging data, and well logging data to establish a lithology columnar section, revealing the symbiotic relationships between various lithologies to be interpreted. Based on the current symbiotic relationships, well logging data is corrected, and a cross-plot between lithology and sensitive well logging curves is plotted. This cross-plot, constrained by the symbiotic relationships between various lithologies to be interpreted, can improve the accuracy and precision of complex lithology interpretation in the study area, supporting efficient exploration and profitable development of deep, complex, and tight oil and gas reservoirs.

[0048] In some embodiments, the method may further include: determining the dominant minerals developed in the study area based on the core section mineral analysis results and / or elemental logging data, and establishing a multi-mineral model; obtaining the mineral content curve of the verification well based on the multi-mineral model and an optimized logging interpretation method using the logging data of the verification well, and constructing a first lithology coding curve; obtaining a second lithology coding curve based on the logging data of the verification well using the current cross plot and coexistence relationship; determining whether the matching degree between the second lithology coding curve and the first lithology coding curve meets the set matching conditions; if not, modifying the current coexistence relationship, correcting the logging data based on the current coexistence relationship, and redrawing the cross plot between lithology and sensitive logging curves until the matching degree between the second lithology coding curve obtained through the current cross plot and the first lithology coding curve meets the set matching conditions.

[0049] Based on a multi-mineral model, an optimized well logging interpretation method is used to construct a first lithology coding curve. Using the first lithology coding curve as a reference, the symbiotic relationships between various lithologies to be interpreted and the cross-plot of complex lithology interpretations are iteratively optimized to further improve the accuracy and precision of complex lithology interpretation.

[0050] In some embodiments, the method further includes obtaining a lithology coding curve for a well with uninterpreted lithology within the study area, based on the well logging data, through cross plots and coexistence relationships.

[0051] Example 2

[0052] Embodiment 2 of this invention provides a specific application of a multi-information-constrained method for interpreting complex lithology. It introduces new geological information such as core mineral analysis, cuttings logging, elemental logging, and imaging logging to construct a symbiotic relationship between deterministic and complex lithology. Through multiple iterations from qualitative cross-plots to quantitative multi-mineral model calculations of mineral percentages, a continuous lithology code curve for the entire well section is obtained, improving the accuracy and precision of complex lithology interpretation and laying the foundation for subsequent research on complex reservoirs.

[0053] Taking the lithological interpretation of the xx2 Sha-3 Member intrusive rock contact metamorphic rock reservoir as an example, the process is as follows: Figure 3 As shown, it includes the following steps:

[0054] Step S31: Collection and processing of well logging, core analysis and testing data and sedimentary facies distribution data.

[0055] We collected and organized sedimentary microfacies maps of different strata in the study area, as well as data from drilled well cuttings logging, elemental logging, conventional logging, imaging logging, and core mineral analysis (thin section, whole-rock elemental analysis, and electron probe microanalysis, etc.). We conducted detailed observations and digitized the core samples from the cored sections. Combined with the sedimentary microfacies maps of the cored sections, we identified the main lithological types, sedimentary environments, and the corresponding sedimentary facies zones and microfacies of the target strata, obtaining a comprehensive lithological columnar section of the cored sections, and further derived lithological coding curves.

[0056] This embodiment clarifies that the main lithologies of the Sha-3 Member are siltstone, mudstone, intrusive rocks, and metamorphic rocks. It is located between the pre-deltaic facies and delta front facies of a semi-deep lacustrine to deep lacustrine sedimentary environment. A comprehensive lithological columnar section of the cored section is established, and lithological coding curves are further obtained. The cored sections of other strata above the Sha-3 Member are studied according to the above method, and lithological coding curves of the cored sections of different strata of the standard well are constructed according to the vertical stratigraphic sedimentary sequence.

[0057] Step S32: Construction of complex lithological symbiotic relationships.

[0058] The mapping relationship between the logging curve response characteristics of different lithologies in the core section and imaging logging images, logging lithology naming, oil and gas shows, and gas logging values ​​was analyzed. Specifically, the intrusive rock with a definitive lithology is diabase (feldspar) rock (typically high pyroxene content, low gamma ray 30 API, and high density 2.73–2.82 g / m³). 3 The upper layers exhibit a high velocity of 5200–5560 m / s and a high resistivity of 57–500 Ω / m, appearing as dark spots in imaging logging. The surrounding rocks consist of thin layers of siltstone and large sets of dark mudstone (medium to high gamma ray, low sonic transit time, and low density, appearing as alternating light and dark layers in imaging logging), and complex lithology in contact metamorphic rocks (with high gamma ray 100 API, high sonic velocity of 4390–5040 m / s, and high density of 2.47–2.63 g / m³). 3 (With resistivity of 15–42 Ω / m and acoustic velocity exhibiting a "step-like" variation), based on the genetic law of contact metamorphic rocks of a certain thickness formed at the top and bottom of the contact surface during thermal baking and hydrothermal action of igneous intrusion into sedimentary host rocks, a symbiotic relationship between intrusive rocks and contact metamorphic rocks was established. See [link to relevant documentation]. Figure 4 As shown.

[0059] Through fitting, if the intrusive rock quality factor is greater than 5.3, the corresponding relationship between the intrusive rock quality factor and the total thickness of the metamorphic rock is as follows:

[0060] h2=18.42×TP-100(3)

[0061] In equation (3), h2 is the total thickness of the metamorphic rock, and TP is the quality factor of the intrusive rock.

[0062] Step S33: Drawing complex lithological intersection plates.

[0063] Based on the aforementioned symbiotic relationship, errors in lithology (such as argillaceous dolomite, dolomitic limestone, carbonate rocks, andesite, rhyolite, and tuff, etc.) in complex lithology sections of the entire well section were corrected to obtain more accurate logging lithology coding curves. On this basis, it was discovered that acoustic and density logging curves are sensitive for identifying intrusive and contact metamorphic rocks. Cross-plots and porosity-permeability interpretations were performed using these two curves to create cross-plots for qualitative interpretation of complex lithology. (See [link to cross-plot]). Figure 5 As shown.

[0064] Step S34: Construction of multi-mineral model.

[0065] First, based on the results of quantitative mineral analysis methods such as electron probe microanalysis (EMPA) and chemical analysis, the main minerals corresponding to the siltstone, intrusive rocks, and metamorphic rocks in the core section of the target layer were determined to be quartz, pyroxene, and zeolite. Second, the mineral analysis results of the core section were compared with the elemental logging data of the corresponding section, the elemental logging data was corrected, and the relationship between the two was established. The elemental logging data of the entire well section was iteratively corrected to determine the main minerals of complex lithology at different depths. Third, an optimized multi-mineral model was established using the determined main minerals (quartz, pyroxene, zeolite), clay, and porosity, and the parameters were optimized by combining the theoretical values ​​of the framework parameters.

[0066] It is understandable that the main mineral here refers to minerals whose average content exceeds the set value.

[0067] Step S35: Calculation of mineral component percentage, porosity, and saturation curves.

[0068] Based on the multi-mineral model established above, the reservoir parameters and relative mineral contents are solved using the Statmin optimization logging interpretation method. The objective function is:

[0069]

[0070] Where Delta is the objective function; n is the number of logging curve series; m i The corrected series response of the i-th logging curve; f i Let τ be the theoretical response of the i-th logging curve; i 2 δ represents the measurement error of the logging value for the i-th logging curve. i 2 Let be the error of the logging response equation for the i-th logging instrument.

[0071] See the calculation results. Figure 6 As shown.

[0072] Step S36: Construct the first lithology coding curve using the percentage curves of major mineral components.

[0073] A detailed interpretation of complex lithology, from qualitative to quantitative analysis, has been achieved.

[0074] Step S37: Based on the logging data of the verification well, obtain the second lithology coding curve using the current cross plot and coexistence relationship.

[0075] Step S38: Determine whether the matching degree between the second lithology coding curve and the first lithology coding curve meets the set matching conditions.

[0076] If yes, the process ends, yielding the final symbiotic relationship between intrusive and metamorphic rocks and a complex lithological intersection chart; if not, proceed to step S39.

[0077] Step S39: Modify the current symbiotic relationship.

[0078] After step S39, return to step S32.

[0079] The second embodiment of this invention has achieved significant results in the interpretation of metamorphic rocks in the contact metamorphic zone of deep intrusive rocks in the xx depression. By introducing data such as core observation, thin section identification, whole-rock elemental analysis, cuttings logging, and imaging logging, the lithology of the igneous rocks in the target layer was clarified as diabase (feldspar) rock (high content of typical mineral pyroxene, low gamma ray, high sonic transit time, high resistivity, and high density), and the surrounding rocks were thin-layered siltstone and a large set of dark mudstone (medium-high gamma ray, low sonic transit time, and low density). Based on the genetic law of the formation of contact metamorphic rocks of a certain thickness at the top and bottom of the contact surface of the igneous intrusion into the sedimentary surrounding rocks by thermal baking and the action of hot fluids, the symbiotic relationship between the intrusive rocks and the contact metamorphic rocks was established. The mineral percentage curves calculated by cross plots and optimized multi-mineral models were iterated to finally obtain the lithology coding curve for the entire well section. The consistency rate of the interpretation of complex lithology reached more than 95%, which effectively solved the problem of fine interpretation of deep complex lithology.

[0080] Based on the inventive concept of this invention, embodiments of this invention also provide a complex lithology interpretation device with multiple information constraints, the structure of which is as follows: Figure 7 As shown, it includes:

[0081] The symbiotic relationship establishment module 71 is used to establish symbiotic relationships between various lithologies to be interpreted based on the orderliness of the vertical stacking and spatial distribution of sedimentary strata, and according to the core data, well logging data and well logging data in the study area.

[0082] The intersection chart drawing module 72 is used to correct logging data based on the current symbiotic relationship and draw an intersection chart between lithology and sensitive logging curves. The intersection chart is used in conjunction with the symbiotic relationship to realize lithological interpretation in the study area.

[0083] In some embodiments, the above-described apparatus further includes a multi-mineral model building module 73, a first lithology coding curve construction module 74, a second lithology coding curve construction module 75, and a judgment module 76.

[0084] The multi-mineral model establishment module 73 is used to determine the main minerals developed in the study area and establish a multi-mineral model based on the mineral analysis results of the core section and / or elemental logging data.

[0085] The first lithology coding curve construction module 74 is used to obtain the mineral content curve of the verification well based on the multi-mineral model and the optimal logging interpretation method, and construct the first lithology coding curve.

[0086] The second lithology coding curve construction module 75 is used to obtain the second lithology coding curve based on the logging data of the verification well, using the current cross plot and coexistence relationship;

[0087] The judgment module 76 is used to determine whether the degree of matching between the second lithology coding curve and the first lithology coding curve meets the set matching conditions;

[0088] If the judgment module 76 determines no, the symbiotic relationship establishment module 71 is also used to modify the current symbiotic relationship; the cross plot drawing module 72 is also used to correct the logging data based on the current symbiotic relationship and redraw the cross plot between lithology and sensitive logging curves until the matching degree between the second lithology coding curve obtained through the current cross plot and the first lithology coding curve meets the set matching conditions.

[0089] In some embodiments, the multi-mineral model building module 73 is used to build the multi-mineral model for:

[0090] Establish a multi-mineral model that includes the main mineral, clay, and porosity.

[0091] In some embodiments, the symbiotic relationship establishment module 71, based on core data, logging data, and well logging data within the study area, establishes various symbiotic relationships among lithologies to be interpreted, for the following purposes:

[0092] Based on core data, well logging data, and well logging data within the study area, the symbiotic relationship between intrusive rocks and metamorphic rocks was established.

[0093] In some embodiments, the symbiotic relationship establishment module 71, which establishes the symbiotic relationship between intrusive rocks and metamorphic rocks based on core data, well logging data, and well logging data within the study area, is used for:

[0094] Based on core data, logging data, and well logging data within the study area, target layer lithology columnar sections were obtained from multiple wells. From the target layer lithology columnar sections of each well, the thickness of the intrusive rock and the total thickness of the metamorphic rocks above and below it were determined. Based on the density, velocity, and thickness of the intrusive rock, the intrusive rock quality factor was determined, resulting in a sample data set containing both the intrusive rock quality factor and the total thickness of the metamorphic rocks. Based on this sample set, the correspondence between the intrusive rock quality factor and the total thickness of the metamorphic rocks was obtained through a fitting method, characterizing the symbiotic relationship between the intrusive rock and the metamorphic rocks.

[0095] In some embodiments, the symbiotic relationship establishment module 71, which determines the intrusive rock quality factor based on the density, velocity, and thickness of the intrusive rock, is used for:

[0096] Based on the density, velocity, and thickness of the intrusive rock, the intrusive rock quality factor is determined using the following formula (1):

[0097] TP=Log(ρ1×v1×h1)(1)

[0098] In equation (1), TP is the intrusive rock quality factor, ρ1 is the intrusive rock density, v1 is the intrusive rock velocity, and h1 is the intrusive rock thickness.

[0099] In some embodiments, the symbiotic relationship establishment module 71, which obtains the correspondence between the intrusive rock quality factor and the total thickness of the metamorphic rock through a fitting method, is used for:

[0100] Through fitting, if the intrusive rock quality factor is greater than a certain value, the corresponding relationship between the intrusive rock quality factor and the total thickness of the metamorphic rock is as follows:

[0101] h2=a×TP-b(2)

[0102] In equation (2), h2 is the total thickness of the metamorphic rock, TP is the quality factor of the intrusive rock, and a and b are constants determined by fitting.

[0103] In some embodiments, the symbiotic relationship establishment module 71, which obtains the correspondence between the intrusive rock quality factor and the total thickness of the metamorphic rock through a fitting method, is used for:

[0104] Through fitting, if the intrusive rock quality factor is greater than 5.3, the corresponding relationship between the intrusive rock quality factor and the total thickness of the metamorphic rock is as follows:

[0105] h2=18.42×TP-100(3)

[0106] In equation (3), h2 is the total thickness of the metamorphic rock, and TP is the quality factor of the intrusive rock.

[0107] In some embodiments, the symbiotic relationship establishment module 71, which obtains a target layer lithology columnar section from multiple wells based on coring data, logging data, and well logging data within the study area, is used for:

[0108] Based on the lithological description data from the core data in the study area, the conventional logging and imaging logging data from the well logging data, and the sedimentary facies distribution data and well test data of the target layer in the study area, lithological columnar diagrams of the target layer from multiple wells were obtained.

[0109] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0110] Based on the inventive concept of the present invention, embodiments of the present invention also provide a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the aforementioned complex lithology interpretation method with multiple information constraints.

[0111] Based on the inventive concept of this invention, embodiments of this invention also provide a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the aforementioned complex lithological interpretation method with multiple information constraints.

[0112] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0113] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0114] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than those stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby clearly incorporated into the detailed description, wherein each claim stands alone as a preferred embodiment of the invention.

[0115] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0116] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0117] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0118] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term “comprising” as used in the specification or claims is interpreted in a manner similar to the term “including,” as it is understood when used as a conjunction in the claims. Additionally, the use of any term “or” in the specification of the claims is intended to mean “non-exclusive or.” The terms “first” and “second” are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

Claims

1. A method for interpreting complex lithology with multiple information constraints, characterized in that, include: Based on the ordered vertical stacking and spatial distribution of sedimentary strata, and using core data, well logging data, and well logging data from the study area, we established symbiotic relationships among various lithologies to be interpreted. Based on the current symbiotic relationship, the logging data is corrected, and a cross plot is drawn between lithology and sensitive logging curves. The cross plot is used in conjunction with the symbiotic relationship to realize the lithological interpretation in the study area.

2. The method according to claim 1, characterized in that, Also includes: Based on the mineral analysis results of the core section and / or elemental logging data, the main minerals developed in the study area are determined, and a multi-mineral model is established; Based on the logging data of the verification well, and using the multi-mineral model, the mineral content curve of the verification well is obtained through the optimal logging interpretation method, and the first lithology coding curve is constructed. Based on the logging data of the test well, the second lithology coding curve is obtained using the current cross plot and coexistence relationship; Determine whether the matching degree between the second lithology coding curve and the first lithology coding curve meets the set matching conditions; If not, modify the current symbiotic relationship, correct the logging data based on the current symbiotic relationship, and redraw the cross plot between lithology and sensitive logging curves until the matching degree between the second lithology coding curve obtained through the current cross plot and the first lithology coding curve meets the set matching conditions.

3. The method according to claim 2, characterized in that, The establishment of the multi-mineral model includes: Establish a multi-mineral model that includes the main mineral, clay, and porosity.

4. The method according to claim 1, characterized in that, Based on core data, well logging data, and well logging data within the study area, various symbiotic relationships among the uninterpreted lithologies were established, including: Based on core data, well logging data, and well logging data within the study area, the symbiotic relationship between intrusive rocks and metamorphic rocks was established.

5. The method according to claim 4, characterized in that, The establishment of the symbiotic relationship between intrusive and metamorphic rocks based on core data, well logging data, and well logging data within the study area includes: Based on the core data, logging data and well logging data in the study area, a target layer lithology columnar section was obtained from multiple wells; From the lithological columnar section of the target layer of each well, the thickness of the intrusive rock and the total thickness of the metamorphic rocks above and below the intrusive rock are determined. Based on the density, velocity and thickness of the intrusive rock, the quality factor of the intrusive rock is determined, and a sample data containing the quality factor of the intrusive rock and the total thickness of the metamorphic rocks is obtained to establish a sample set. Based on the sample set, the correspondence between the quality factor of intrusive rocks and the total thickness of metamorphic rocks is obtained by fitting, which characterizes the symbiotic relationship between intrusive rocks and metamorphic rocks.

6. The method according to claim 5, characterized in that, The determination of intrusive rock quality factors based on the density, velocity, and thickness of the intrusive rock includes: Based on the density, velocity, and thickness of the intrusive rock, the intrusive rock quality factor is determined using the following formula (1): TP=Log(ρ1×v1×h1) (1) In equation (1), TP is the intrusive rock quality factor, ρ1 is the intrusive rock density, v1 is the intrusive rock velocity, and h1 is the intrusive rock thickness.

7. The method according to claim 5, characterized in that, The method of obtaining the correspondence between intrusive rock quality factors and total metamorphic rock thickness through fitting includes: Through fitting, if the intrusive rock quality factor is greater than a certain value, the corresponding relationship between the intrusive rock quality factor and the total thickness of the metamorphic rock is as follows: h2=a×TP-b (2) In equation (2), h2 is the total thickness of the metamorphic rock, TP is the quality factor of the intrusive rock, and a and b are constants determined by fitting.

8. The method according to claim 5, characterized in that, The method of obtaining the correspondence between intrusive rock quality factors and total metamorphic rock thickness through fitting includes: Through fitting, if the intrusive rock quality factor is greater than 5.3, the corresponding relationship between the intrusive rock quality factor and the total thickness of the metamorphic rock is as follows: h2=18.42×TP-100 (3) In equation (3), h2 is the total thickness of the metamorphic rock, and TP is the quality factor of the intrusive rock.

9. The method according to claim 5, characterized in that, Based on core data, logging data, and well logging data within the study area, a target lithology columnar section of multiple wells is obtained, including: Based on the lithological description data from the core data in the study area, the conventional logging and imaging logging data from the well logging data, and the sedimentary facies distribution data and well test data of the target layer in the study area, lithological columnar diagrams of the target layer from multiple wells were obtained.

10. The method according to any one of claims 1 to 9, characterized in that, Also includes: For wells in the study area with uninterpreted lithology, the lithology coding curve of the well is obtained by cross plots and coexistence relationships based on the well logging data.

11. A complex lithological interpretation device with multiple information constraints, characterized in that, include: The symbiotic relationship establishment module is used to establish symbiotic relationships between various lithologies to be interpreted based on the orderliness of the vertical stacking and spatial distribution of sedimentary strata, and according to core data, well logging data and well logging data in the study area. The cross-plot drawing module is used to correct logging data based on the current symbiotic relationship and draw cross-plots between lithology and sensitive logging curves. The cross-plots are used in conjunction with the symbiotic relationship to realize lithological interpretation in the study area.

12. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the complex lithology interpretation method with multiple information constraints as described in any one of claims 1 to 10.

13. A server, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the complex lithological interpretation method with multiple information constraints as described in any one of claims 1 to 10.

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