Source rock quantitative prediction method based on seismic data inversion

Abstract

The invention provides a source rock quantitative prediction method based on seismic data inversion, and relates to the technical field of oil-gas exploration, and the method comprises the steps: building a relation model of total organic carbon and longitudinal wave impedance for actually measured total organic carbon sampling data and actually measured logging data of a target interval; based on the actually measured logging data, performing inversion by using the original seismic data of the target interval to obtain a longitudinal wave impedance body of the target interval; according to actually measured logging data and original seismic data, carrying out intersection analysis on lithology and longitudinal wave impedance of a target interval, and determining a hydrocarbon source rock longitudinal wave impedance threshold value; the relation model and the longitudinal wave impedance body are combined to obtain hydrocarbon source rock total organic carbon content distribution data of the target interval; and in combination with the longitudinal wave impedance body and the longitudinal wave hydrocarbon source rock impedance threshold value, obtaining hydrocarbon source rock thickness distribution data of the target interval. Reliable basis is provided for well location deployment for efficient exploration and development, exploration and development benefits are improved, and the method is not limited by regions and is wide in application range.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology and is applied to the quantitative prediction of source rocks. Specifically, it relates to a method for quantitative prediction of source rocks based on seismic data inversion. Background Technology

[0002] Source rock evaluation is fundamental to the evaluation of hydrocarbon-generating depressions and blocks. Evaluation parameters include source rock scale, source rock quality (total organic carbon content (TOC), organic matter type, chloroform bitumen "A" content, total hydrocarbon content, etc.), and hydrocarbon generation and expulsion rates. Among these, the scale of source rock development, TOC content, and hydrocarbon generation and expulsion rates are important indicators for evaluating the hydrocarbon generation potential of depressions. Therefore, identifying source rocks, characterizing their spatial distribution, and evaluating their quality are crucial issues that need to be addressed in oil and gas exploration.

[0003] In past studies, geochemical analysis methods could only be applied when rock samples and cuttings data were available. Furthermore, these methods were limited by the number of samples and sampling intervals, often leading to incomplete or biased results. In recent years, some experts and scholars have gradually developed research on source rock evaluation based on geophysical methods, achieving some progress. However, previous methods for source rock evaluation and prediction were limited to single-well geochemical analysis of source rocks, using well logging data for TOC evaluation. These methods could not predict spatial distribution, or, based on seismic attributes, could only qualitatively characterize the distribution of source rocks, failing to quantitatively characterize the spatial distribution of source rock thickness and TOC content.

[0004] To address the problems of existing technologies, this invention provides a quantitative prediction method for hydrocarbon source rocks based on seismic data inversion. Summary of the Invention

[0005] To address the problems of existing technologies, this invention provides a quantitative prediction method for hydrocarbon source rocks based on seismic data inversion, the method comprising the following steps:

[0006] Based on the measured total organic carbon sampling data and measured well logging data of the target layer, a relationship model between total organic carbon and P-wave impedance was established.

[0007] Based on the measured well logging data, the P-wave impedance volume of the target layer is obtained by inversion using the original seismic data of the target layer;

[0008] Based on the measured well logging data and the original seismic data, the lithology and P-wave impedance of the target layer are analyzed to determine the P-wave impedance threshold of the source rock.

[0009] By combining the aforementioned relationship model and the aforementioned longitudinal wave impedance body, the distribution data of total organic carbon content in the source rocks of the target layer were obtained;

[0010] By combining the longitudinal wave impedance volume and the longitudinal wave source rock impedance threshold, the source rock thickness distribution data of the target layer is obtained.

[0011] According to one embodiment of the present invention, the relationship model between total organic carbon and longitudinal wave impedance is established through the following steps:

[0012] Based on the measured total organic carbon sampling data and measured well logging data, determine the sensitive parameters that are sensitive to total organic carbon;

[0013] At least two prediction methods are used to predict the total organic carbon content, and the optimal prediction method is determined.

[0014] A cross-analysis of total organic carbon and longitudinal wave impedance was performed to establish a fitting relationship expression between total organic carbon and longitudinal wave impedance, which serves as the relationship model.

[0015] According to one embodiment of the present invention, the sensitive parameter sensitive to total organic carbon is determined by the following steps: by performing cross-analysis of the measured total organic carbon sampling data with the sonic transit time logging data, density logging data, natural gamma logging data, and resistivity logging data in the measured logging data, the sensitive parameter sensitive to total organic carbon is determined.

[0016] According to an embodiment of the present invention, the prediction method includes, but is not limited to: ΔlogR prediction method, multivariate fitting clustering analysis method, and neural network prediction method.

[0017] According to an embodiment of the present invention, the P-wave impedance volume is obtained through the following steps: based on the standardization of the measured logging data, well-seismic calibration, wavelet extraction and low-frequency model establishment are performed, and constrained sparse pulse inversion is carried out to obtain the P-wave impedance volume of the target layer in the study area.

[0018] According to one embodiment of the present invention, the P-wave impedance threshold of the source rock is determined by the following steps: performing cross-analysis of the lithology and P-wave impedance of the target layer to determine the P-wave impedance threshold of mudstone and argillaceous carbonate rock, which is used as the P-wave impedance threshold of the source rock.

[0019] According to an embodiment of the present invention, the total organic carbon content distribution data of the source rock is obtained through the following steps: using the relational model, the longitudinal wave impedance volume is converted into a total organic carbon data volume, the attributes of the total organic carbon data volume are extracted along the layers to characterize the distribution of total organic carbon, and the total organic carbon content distribution data of the source rock is obtained.

[0020] According to an embodiment of the present invention, the thickness distribution data of the source rock is obtained by the following steps: using the longitudinal wave impedance threshold of the source rock and the longitudinal wave impedance volume, the spatial distribution of the thickness of the source rock is characterized, thereby obtaining the thickness distribution data of the source rock.

[0021] According to another aspect of the invention, a storage medium is also provided, which includes a series of instructions for performing the steps of the method described in any of the preceding claims.

[0022] According to another aspect of the present invention, a quantitative prediction system for source rocks based on seismic data inversion is also provided, which performs the method as described in any of the preceding claims, the system comprising:

[0023] The relational model module establishes a relational model between total organic carbon and P-wave impedance based on the measured total organic carbon sampling data and measured well logging data of the target layer.

[0024] The P-wave impedance module is based on the measured well logging data and uses the original seismic data of the target layer to invert the P-wave impedance of the target layer.

[0025] The source rock P-wave impedance threshold module, based on the measured well logging data and the original seismic data, performs a cross-analysis of the lithology and P-wave impedance of the target layer to determine the P-wave impedance threshold of the source rock.

[0026] The total organic carbon content distribution module, in conjunction with the aforementioned relationship model and the longitudinal wave impedance body, obtains the total organic carbon content distribution data of the source rocks in the target layer.

[0027] The source rock thickness distribution module combines the longitudinal wave impedance volume and the longitudinal wave source rock impedance threshold to obtain the source rock thickness distribution data of the target layer.

[0028] This invention provides a quantitative prediction method for source rocks based on seismic data inversion. Compared with existing technologies, it has the following advantages: This invention can perform quantitative prediction of source rocks, characterize the spatial distribution of source rocks through source rock thickness distribution data, clarify the scale of oil and gas resources and favorable exploration directions through source rock total organic carbon content distribution data, and quantitatively evaluate the quality and volume of source rocks by combining source rock thickness distribution data and source rock total organic carbon content distribution data. This provides a reliable basis for well location deployment for efficient exploration and development, improves exploration and development efficiency, and is not limited by region, with a wide range of applications.

[0029] 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 description, claims, and drawings. Attached Figure Description

[0030] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0031] Figure 1 A flowchart illustrating the steps of a quantitative prediction method for hydrocarbon source rocks based on seismic data inversion according to an embodiment of the present invention is shown.

[0032] Figure 2 A flowchart illustrating a quantitative prediction method for hydrocarbon source rocks based on seismic data inversion according to an embodiment of the present invention is shown.

[0033] Figure 3 The diagram shows the intersection analysis of measured TOC and logging curves in the Yuertus source rock section of wells TS5, LT1 and LT3 according to an embodiment of the present invention.

[0034] Figures 4a-4b This figure shows a comparison of TOC calculated and measured TOC using two prediction methods according to an embodiment of the present invention.

[0035] Figure 5 This shows a TOC and P-wave impedance intersection analysis diagram according to an embodiment of the present invention;

[0036] Figure 6 This shows a cross-analysis diagram of lithology and impedance of the Yuertus Formation in well TS5 according to an embodiment of the present invention;

[0037] Figure 7 This shows a longitudinal wave impedance profile obtained by inversion through a TS5 well according to an embodiment of the present invention;

[0038] Figure 8 A planar distribution map of TOC content in the Cambrian Yuertus source rocks in the northern Tarim Basin, according to an embodiment of the present invention, is shown.

[0039] Figure 9 A planar distribution map of the thickness of the Cambrian Yuertus source rocks in the northern Tarim Basin is shown, according to an embodiment of the present invention.

[0040] In the accompanying drawings, the same parts use the same reference numerals. Also, the drawings are not drawn to scale. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0042] The invention patent application "A Method and Equipment for Analyzing the Main Controlling Factors of Shale Oil Production and Predicting Seismic Events" (CN104199108A) filed by Wang Zhongtao et al. (2014) establishes a shale oil and gas reservoir accumulation model through analysis of shale oil and gas accumulation conditions, oil and gas geological characteristics, and oil and gas accumulation characteristics. It then selects targeted seismic prediction techniques for prediction, ultimately achieving comprehensive prediction of oil and gas enrichment areas in the Langzhong shale oil reservoir. The method is as follows: The thickness of the Da'anzhai II shale is predicted using well-constrained wave impedance inversion; research is conducted on methods for obtaining shale elastic parameters through simultaneous pre-stack P-wave inversion and multi-wave joint inversion; and a method for predicting shale brittle parameters is established by inverting petrophysical parameters such as bulk modulus, shear modulus, Lamé constant, and Poisson's ratio; combined with source rock prediction and evaluation, thin-layer limestone distribution evaluation, and micro-fracture distribution evaluation, comprehensive prediction and evaluation of favorable shale areas are achieved. The existing technology (CN104199108A) uses seismic prediction technology to predict the thickness and rock physical parameters of source rocks, but it cannot characterize the TOC content of source rocks.

[0043] The invention patent application "Method and Device for Predicting Source Rocks Based on Probabilistic Neural Network Algorithm" proposed by Yuan Xiaoyu et al. (2016), publication number CN107436452A, includes the following method: analyzing and testing source rock core and cuttings samples obtained from various wells in the study area to obtain the Toc value of the sample points; calculating the longitudinal Toc prediction curve of the source rock strata in each well in the study area using well logging curves; correcting the Toc prediction curve based on the Toc value of the sample points to obtain the Toc curve; training the seismic attributes using a probabilistic neural network algorithm based on the Toc curve and the seismic attributes of the well-side seismic traces to obtain the preferred seismic attributes; extracting the preferred seismic attributes from the three-dimensional seismic data; and calculating the three-dimensional distributed Toc data volume to complete the prediction of source rocks in the study area. This prior art (CN107436452A) predicts the TOC content of source rocks, but can only qualitatively predict the thickness of source rocks, not quantitatively.

[0044] The invention patent proposed by Wen Huaguo et al. (2020), entitled "A Method for Predicting Hydrocarbon Source Rocks by Restoring Paleoenvironment", with application publication number CN112133374A, includes the following methods: (1) determining the content of each mineral; (2) using the elemental combination forms of Sr / Ba, B / Ga, Th / U, Fe / Mn, and Sr / Ca to determine whether the sedimentary environment was marine or non-marine; (3) using boron to determine the specific value of paleosalinity and comparing it with the current normal seawater value to infer whether the sedimentary environment at that time was saline or non-saline; (4) using the elemental combination forms of (Cu+Mo) / Zn and V / (V+Ni) to determine whether the sedimentary environment was oxidizing or reducing; (5) conducting a comprehensive analysis of the sedimentary environment, restoring the paleo-sedimentary environment and source-reservoir configuration relationship, and analyzing the shale development and distribution pattern accordingly, pointing out favorable hydrocarbon accumulation combinations, and finally combining drilling, oil testing, and logging data to conduct hydrocarbon source rock evaluation, and finally predicting the favorable areas for the development and distribution of relatively high-quality hydrocarbon source rocks. The existing technology (CN112133374A) can only characterize source rocks in a single well, but cannot accurately predict the spatial distribution of source rocks.

[0045] The invention patent application "A Method and Device for Predicting Total Organic Carbon Content in Source Rocks" (CN113219553A) filed by Liu Chenglin et al. (2021) uses a Bayesian neural network method based on principal component analysis for TOC prediction. By training with different salinity models and optimizing the method, the prediction accuracy of TOC is improved, solving the problem of predicting source rocks under salinized conditions. Furthermore, when well logging curves are missing, a ΔlogR model optimized by a supplementary method is used for prediction to meet the accuracy requirements of TOC prediction in areas with insufficient data. This prior art (CN113219553A) can only evaluate source rocks from single wells and cannot predict the spatial distribution of source rocks.

[0046] The above-mentioned technical background survey shows that existing technologies for evaluating and predicting source rocks are limited to geochemical analysis of source rocks in a single well and TOC evaluation using well logging data. They cannot predict spatially, or they can only qualitatively characterize the distribution of source rocks through seismic attributes, but cannot quantitatively characterize the spatial distribution of source rock thickness and TOC content.

[0047] To address the aforementioned deficiencies in existing technologies, this invention aims to solve the technical problem of insufficient prediction of TOC and spatial distribution of source rocks. By using single-well and seismic data inversion methods, it resolves the spatial distribution and TOC content of source rocks, predicts favorable source rock distributions, and quantitatively evaluates the quality and volume of source rocks, providing important data for oil and gas exploration.

[0048] Figure 1 A flowchart illustrating the steps of a quantitative prediction method for source rocks based on seismic data inversion according to an embodiment of the present invention is shown.

[0049] like Figure 1 As shown, in step S11, a relationship model between total organic carbon and longitudinal wave impedance is established based on the measured total organic carbon sampling data and measured well logging data of the target layer.

[0050] In one embodiment, by means of Figure 2 The steps S21-S23 shown establish a model relating total organic carbon to longitudinal wave impedance.

[0051] like Figure 2 As shown, in step S21, the TOC (Total Organic Carbon) sensitivity curve is optimized. Specifically, based on the measured total organic carbon sampling data and measured well logging data, a sensitive parameter sensitive to total organic carbon is determined.

[0052] In one embodiment, the sensitive parameters for total organic carbon (TOC) are determined through the following steps: By performing cross-analysis of measured TOC sampling data with measured well logging data including sonic transit time, density, natural gamma ray, and resistivity, the sensitive parameters for TOC are determined. Specifically, by cross-analyzing measured TOC sampling point data from a single well with well logging data such as sonic transit time, density, GR, P-wave impedance, and resistivity, the most sensitive TOC-sensitive curve is selected. Figure 3 ).

[0053] like Figure 2 As shown, in step S22, the TOC calculation method is optimized and calculated. Specifically, at least two prediction methods are used to predict the total organic carbon content, and the optimal prediction method is determined.

[0054] In one embodiment, the prediction method includes, but is not limited to, the ΔlogR prediction method, the multivariate fitting clustering analysis method, and the neural network prediction method. Specifically, the TOC content of a single well is predicted using methods such as ΔlogR, multivariate fitting, and neural networks. Through cross-analysis with the measured TOC sampling points of the single well, the preferred method for calculating the TOC content of the single well is selected, and the TOC content of the single well is calculated. Figures 4a-4b ).

[0055] In practical applications, the ΔlogR technique for calculating organic carbon (TOC) is a method different from that obtained through traditional geochemical testing. This method utilizes the response characteristics of source rock organic carbon content on well logging curves (sonic transit time, resistivity) to establish a well logging interpretation model, calculate the continuous distribution value of TOC, and thus obtain accurate organic carbon content, supplementing the deficiencies of core data.

[0056] like Figure 2As shown, in step S23, the mathematical relationship between TOC and P-wave impedance is established. Specifically, a cross-analysis of total organic carbon and P-wave impedance is performed to establish a fitting relationship expression between total organic carbon and P-wave impedance, which serves as the relationship model. Furthermore, through the cross-analysis of TOC and P-wave impedance, a fitting relationship between the two is established (…). Figure 5 ).

[0057] like Figure 1 As shown, in step S12, the P-wave impedance volume of the target segment is obtained by inversion using the original seismic data of the target segment based on the measured well logging data. Specifically, the P-wave impedance volume is obtained through the following steps: based on the standardization of the measured well logging data, well-seismic calibration, wavelet extraction, and low-frequency model establishment are performed, and constrained sparse pulse inversion is carried out to obtain the P-wave impedance volume of the target segment in the study area.

[0058] In one embodiment, by means of Figure 2 Steps S24-S26, as shown, yield the longitudinal wave impedance of the target layer. For example... Figure 2 As shown, in step S24, the density, acoustic logging curves of the target layer of the source rock in the study area are standardized, the well is calibrated and the wavelet is extracted. In step S25, post-stack constrained sparse pulse inversion is carried out, and in step S26, the longitudinal wave impedance volume of the target layer of the source rock is obtained.

[0059] like Figure 1 As shown, in step S13, based on measured well logging data and raw seismic data, a cross-analysis of the lithology and P-wave impedance of the target layer is performed to determine the P-wave impedance threshold of the source rock. Specifically, the P-wave impedance threshold of the source rock is determined through the following steps: a cross-analysis of the lithology and P-wave impedance of the target layer is performed to determine the P-wave impedance threshold of mudstone and argillaceous carbonate rocks, which are then used as the P-wave impedance threshold of the source rock.

[0060] In one embodiment, by means of Figure 2 Step S27, as shown, determines the longitudinal wave impedance threshold of the source rock. For example... Figure 2 As shown, in step S27, the P-wave impedance threshold of the mudstone is determined. Specifically, the P-wave impedance threshold of the source rock is determined by analyzing the source rock strata of a single well. Figure 6 The spatial distribution of source rocks can be predicted using the longitudinal wave impedance volume obtained by inversion.

[0061] like Figure 1As shown, in step S14, the total organic carbon (TOC) content distribution data of the source rock in the target layer is obtained by combining the relational model and the P-wave impedance volume. Specifically, the TOC content distribution data of the source rock is obtained through the following steps: using the relational model, the P-wave impedance volume is converted into a TOC data volume; attributes are extracted along the layer from the TOC data volume to characterize the distribution of TOC, thus obtaining the TOC content distribution data of the source rock.

[0062] In one embodiment, by means of Figure 2 Step S28, as shown, yields the distribution data of total organic carbon content in the source rocks of the target layer. For example... Figure 2 As shown, in step S28, the TOC data volume is generated. Specifically, based on the established fitting relationship between TOC and P-wave impedance, the P-wave impedance volume is converted into a TOC data volume. Attributes are extracted from the TOC data volume along the seismic reflection layer to predict the distribution of TOC content in the source rock.

[0063] like Figure 1 As shown, in step S15, the source rock thickness distribution data of the target layer is obtained by combining the P-wave impedance volume and the P-wave source rock impedance threshold. Specifically, the source rock thickness distribution data is obtained through the following steps: using the source rock P-wave impedance threshold and the P-wave impedance volume, the spatial distribution of the source rock thickness is characterized to obtain the source rock thickness distribution data.

[0064] In one embodiment, by means of Figure 2 Step S29, as shown, yields the quantitative prediction results of the source rock. For example... Figure 2 As shown, in step S29, the thickness and TOC content of the source rock are predicted. Specifically, a comprehensive analysis of the source rock potential is performed. Based on the source rock thickness distribution obtained in step S15 and the source rock TOC content distribution obtained in step S14, the spatial distribution and potential of high-quality source rocks are comprehensively analyzed.

[0065] This invention provides a quantitative prediction method for source rocks based on seismic data inversion. It optimizes TOC-sensitive logging curves, selects appropriate TOC calculation methods, and calculates the TOC content of individual wells. Through cross-analysis, a mathematical relationship between TOC and P-wave impedance is established. Based on single-well analysis, the P-wave impedance threshold of the source rock is determined, and a P-wave impedance volume is obtained using seismic data inversion to characterize the spatial distribution of source rock thickness. Based on the mathematical relationship between TOC and P-wave impedance, the total organic carbon (TOC) data volume is obtained, thereby achieving the goal of quantitatively predicting and evaluating source rocks.

[0066] In one embodiment, the quantitative prediction method for source rocks based on seismic data inversion provided by this invention is applied to the prediction of source rocks in the Cambrian Yuertus Formation in the northern Tarim Basin. The Yuertus Formation in the northern Tarim Basin mainly consists of argillaceous limestone to calcareous mudstone, with the calcareous mudstone being the primary source rock. It is overlain by the Xiaoerbulake Formation dolomite and underlain by Sinian dolomite. Three wells, TS5, LT1, and LT3, encountered Yuertus source rocks in the study area, with calcareous mudstone sections of thicknesses of 32.2 meters, 18.01 meters, and 15.28 meters, respectively. The average TOC contents at sampling points TS5 and LT1 were 5.05% and 4.08%, respectively.

[0067] Analysis of the intersection of measured TOC and well logging curves at sampling points in the Yuertus source rock section encountered in the study area revealed that high TOC zones are characterized by high GR, high sonic transit time, high resistivity, and low density. The TOC values ​​showed a high correlation with these logging curves. Figure 3 As shown, the correlation coefficients between TOC and GR, acoustic transit time, resistivity, and density are 0.8, 0.81, 0.7, and 0.81, respectively. Therefore, the analysis suggests that acoustic transit time, density, and GR are sensitive curves for TOC.

[0068] To obtain the TOC content of the source rock strata, ΔlogR, various types of curve clustering analysis, and neural network methods were used to simulate and calculate the TOC of a single well. Cross-analysis of the calculated results and the measured TOC showed a high correlation between the clustering analysis results and the measured results (correlation coefficient of 0.8). Figures 4a-4b As shown, the correlation coefficients between the TOC calculated by the cluster analysis method and the measured results and the actual results are 0.8 and 0.62, respectively. Therefore, the correlation between the TOC calculated by the cluster analysis method and the actual results is good.

[0069] Analysis of the intersection of single-well longitudinal wave impedance and TOC ( Figure 5 As can be seen, the longitudinal wave impedance has a strong correlation with TOC (correlation coefficient reaches 0.9). Therefore, based on the fitting relationship between TOC and longitudinal wave impedance (TOC = 0.279822 - 1.69015e08 * PIMP), the inverted longitudinal wave impedance volume is converted into TOC data. Figure 5 As shown, the correlation coefficient between TOC and P-wave impedance of the Yuertu Formation in Well LT1 is 0.9, and the mathematical relationship between TOC and P-wave impedance is: TOC=0.279822-1.69015e08*PIMP.

[0070] Through the cross-analysis of lithology and P-wave impedance of the Cambrian Yuertus Formation ( Figure 6 It can be seen that the P-wave impedance can distinguish mudstone (high-quality source rock) from argillaceous carbonate rocks (the lithological threshold for P-wave impedance is 1.35e7 kg / m). 3*m / s), providing a basis for predicting the distribution of source rocks in the Yuertus region using P-wave impedance. For example... Figure 6 As shown, the longitudinal wave impedance can distinguish between the mudstone and marl of the Cambrian Yuertus Formation, with a threshold of 1.35e7 kg / m. 3 *m / s, used as the longitudinal wave impedance threshold of the source rock.

[0071] Based on the standardization of the logging curves of TS5, LT1, and LT3 wells in the Yuertus Formation encountered in the study area, well-seismic calibration, wavelet extraction, and low-frequency model establishment were performed. Constrained sparse pulse inversion was then conducted to obtain the P-wave impedance volume of the target interval in the study area. The inversion results show that the inverted P-wave impedance of the Yuertus source rock interval matches the measured values ​​from the well. Figure 7 The inversion results are reliable.

[0072] Using the above-mentioned relationship between TOC and P-wave impedance fitting, the P-wave impedance volume is converted into a TOC data volume, and attributes are extracted along the layers to characterize the TOC distribution. Figure 8 The predicted source rock TOCs at well points TS5, LT1, and LT3 are 5.7, 5.25, and 5.2, respectively. The errors between the predicted source rock TOCs at well points TS5 and LT1 and the measured TOCs are 0.65 and 1.17, respectively.

[0073] Using the threshold values ​​of mudstone obtained from the preceding lithological analysis and the P-wave impedance volume obtained from the inversion, the spatial distribution of the predicted thickness of the Yurtus source rock is characterized. Figure 9 The predicted source rock thicknesses at well points TS5, LT1, and LT3 are 32 meters, 16 meters, and 14 meters, respectively, with errors of -0.2 meters, -2.01 meters, and -1.28 meters from the actual thicknesses.

[0074] It should be noted that, Figure 8 and Figure 9 This is a schematic diagram of TOC distribution and thickness spatial distribution, used only as an example to illustrate the characterization results. Due to resolution and display limitations, Figure 8 and Figure 9 The inability to fully reproduce all details of the engraving result (e.g., some overlapping text in the image) does not mean that the disclosure of this embodiment is unclear. In practical applications, those skilled in the art can obtain a clear engraving result image by using appropriate tools (e.g., software tools) in accordance with the disclosure of this invention.

[0075] The predicted source rock thickness and TOC content are consistent with the actual drilled wells. In terms of planar distribution, the areas with thick Yuertus source rocks and high TOC content exhibit a near-east-west oriented ring distribution. Well TS5 is located in the northern part of the Tarim Oilfield. TS5 and its southern region are areas with thick Yuertus Formation source rocks and high TOC content, coinciding with the high-yield oil-producing areas of the Tarim Oilfield.

[0076] This invention provides a quantitative prediction method for source rocks based on seismic data inversion. It optimizes TOC-sensitive logging curves, selects appropriate TOC calculation methods, and calculates the TOC content of individual wells. Through cross-analysis, a mathematical relationship between TOC and P-wave impedance is established. Based on the single-well analysis, the P-wave impedance threshold of the source rock is determined, and the P-wave impedance volume is obtained using seismic data inversion to characterize the spatial distribution of source rock thickness. Based on the mathematical relationship between TOC and P-wave impedance, the total organic carbon (TOC) data volume is obtained, thereby achieving the goal of quantitatively predicting and evaluating source rocks.

[0077] The quantitative prediction method for source rocks based on seismic data inversion provided by this invention can also be used in conjunction with a computer-readable storage medium. The storage medium stores a computer program, which is executed to run the quantitative prediction method for source rocks based on seismic data inversion. The computer program can execute computer instructions, which include computer program code. The computer program code can be in the form of source code, object code, executable file, or some intermediate form.

[0078] Computer-readable storage media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0079] It should be noted that the contents of computer-readable storage media may be appropriately added to or subtracted from the contents according to the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media may not include electrical carrier signals and telecommunication signals.

[0080] According to another aspect of the present invention, a quantitative prediction system for source rocks based on seismic data inversion is also provided, which executes a quantitative prediction method for source rocks based on seismic data inversion. The system includes: a relational model module, a P-wave impedance volume module, a source rock P-wave impedance threshold module, a total organic carbon content distribution module, and a source rock thickness distribution module.

[0081] In one embodiment, the relational model module establishes a relational model between total organic carbon (TOC) and P-wave impedance based on measured TOC sampling data and measured well logging data of the target layer; the P-wave impedance volume module obtains the P-wave impedance volume of the target layer by inverting the original seismic data of the target layer based on the measured well logging data; the source rock P-wave impedance threshold module performs an interpolation analysis of lithology and P-wave impedance of the target layer based on the measured well logging data and the original seismic data to determine the P-wave impedance threshold of the source rock; the total organic carbon content distribution module combines the relational model and the P-wave impedance volume to obtain the total organic carbon content distribution data of the source rock in the target layer; and the source rock thickness distribution module combines the P-wave impedance volume and the P-wave source rock impedance threshold to obtain the source rock thickness distribution data of the target layer.

[0082] In summary, this invention provides a quantitative prediction method for source rocks based on seismic data inversion. Compared with existing technologies, it has the following advantages: This invention can perform quantitative prediction of source rocks, characterize the spatial distribution of source rocks through source rock thickness distribution data, clarify the scale of oil and gas resources and favorable exploration directions through source rock total organic carbon content distribution data, and quantitatively evaluate the quality and volume of source rocks by combining source rock thickness distribution data and source rock total organic carbon content distribution data. This provides a reliable basis for well location deployment for efficient exploration and development, improves exploration and development efficiency, and is not limited by region, with a wide range of applications.

[0083] It should be understood that the embodiments disclosed herein are not limited to the specific structures, processing steps, or materials disclosed herein, but should be extended to equivalent substitutions of these features as understood by those skilled in the art. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0084] In the description of this invention, unless otherwise stated, "a plurality of" means two or more; the terms "upper," "lower," "left," "right," "inner," "outer," "front end," "rear end," "head," "tail," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0085] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0086] Certain terms are used throughout this application to refer to specific system components. As those skilled in the art will recognize, the same components may often be referred to by different names, and therefore this application is not intended to distinguish those components that differ only in name and not in function. In this application, the terms “comprise,” “include,” and “have” are used in an open-ended manner and should therefore be interpreted as meaning “including, but not limited to…”. Furthermore, the terms “substantially,” “materially,” or “approximately” as used herein refer to industry-accepted tolerances for the corresponding terms. The term “coupling,” as may be used herein, includes direct coupling and indirect coupling via additional components, elements, circuits, or modules, wherein, for indirect coupling, the intermediate component, element, circuit, or module does not alter the information of the signal but may adjust its current level, voltage level, and / or power level. Inferred coupling (e.g., one element is inferredly coupled to another element) includes direct and indirect coupling between two elements in the same manner as “coupling.”

[0087] The phrase "an embodiment" or "an embodiment" used in this specification means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Therefore, the phrase "an embodiment" or "an embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0088] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

[0089] While the embodiments disclosed in this invention are as described above, the content is merely for the purpose of facilitating understanding of the invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and variations in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection for this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method for quantitative prediction of source rocks based on seismic data inversion, characterized in that, The method comprises the following steps: The measured total organic carbon sampling data and the measured logging data of the target interval are used to establish a relationship model between total organic carbon and P-wave impedance; Based on the measured logging data, the P-wave impedance volume of the target interval is obtained by using the original seismic data of the target interval; The lithology and P-wave impedance of the target interval are cross-analyzed according to the measured logging data and the original seismic data, and the P-wave impedance threshold of source rock is determined; The total organic carbon content distribution data of source rock in the target interval are obtained by combining the relationship model and the P-wave impedance volume; The thickness distribution data of source rock in the target interval are obtained by combining the P-wave impedance volume and the P-wave source rock impedance threshold.

2. The method for quantitative prediction of source rock based on seismic data inversion according to claim 1, characterized in that, The relationship model between total organic carbon and P-wave impedance is established by the following steps: The sensitive parameters sensitive to total organic carbon are determined according to the measured total organic carbon sampling data and the measured logging data; At least two prediction methods are used to predict the total organic carbon content, and the optimal prediction method is determined; The fitting relationship expression between total organic carbon and P-wave impedance is established by cross-analyzing total organic carbon and P-wave impedance, which is used as the relationship model.

3. The method for quantitative prediction of source rock based on seismic data inversion according to claim 2, characterized in that, The sensitive parameters sensitive to total organic carbon are determined by cross-analyzing the measured total organic carbon sampling data and the acoustic travel time logging data, the density logging data, the natural gamma logging data and the resistivity logging data in the measured logging data.

4. The method for quantitative prediction of source rock based on seismic data inversion according to claim 2 or 3, characterized in that, The prediction methods include but are not limited to the ΔlogR prediction method, the multivariate fitting cluster analysis method and the neural network prediction method.

5. The method for quantitative prediction of source rocks based on seismic data inversion according to any one of claims 1-4, characterized in that, The P-wave impedance volume is obtained by the following steps: on the basis of standardizing the measured logging data, well-to-seismic calibration, wavelet extraction and low-frequency model establishment are carried out, and constrained sparse pulse inversion is carried out to obtain the P-wave impedance volume of the target interval in the study area.

6. The method for quantitative prediction of source rocks based on seismic data inversion according to any one of claims 1-5, characterized in that, The P-wave impedance threshold of source rock is determined by cross-analyzing the lithology and P-wave impedance of the target interval, and the P-wave impedance threshold of mudstone and argillaceous carbonate rock is determined as the P-wave impedance threshold of source rock.

7. The method for quantitative prediction of source rocks based on seismic data inversion according to any one of claims 1-6, characterized in that, The total organic carbon content distribution data of source rock are obtained by converting the P-wave impedance volume into total organic carbon data volume by using the relationship model, extracting the properties of the total organic carbon data volume along the layer, depicting the distribution of total organic carbon, and obtaining the total organic carbon content distribution data of source rock.

8. The method for quantitative prediction of source rocks based on seismic data inversion according to any one of claims 1-7, characterized in that, The thickness distribution data of source rock are obtained by using the P-wave impedance threshold of source rock and the P-wave impedance volume to depict the thickness spatial distribution of source rock, and obtaining the thickness distribution data of source rock.

9. A storage medium, characterized by It comprises a series of instructions for executing the method steps as claimed in any one of claims 1-8.

10. A system for quantitative prediction of source rocks based on seismic data inversion, characterized in that, The system comprises: A relationship model module is used to establish a relationship model between total organic carbon and P-wave impedance according to the measured total organic carbon sampling data and the measured logging data of the target interval; A P-wave impedance volume module is used to obtain the P-wave impedance volume of the target interval by using the original seismic data of the target interval based on the measured logging data; A P-wave impedance threshold module is used to determine the P-wave impedance threshold of source rock by cross-analyzing the lithology and P-wave impedance of the target interval; a hydrocarbon source rock P-wave impedance threshold module, which performs cross analysis on lithology and P-wave impedance of a target interval according to the measured logging data and the original seismic data, and determines a hydrocarbon source rock P-wave impedance threshold; a total organic carbon content distribution module, which obtains total organic carbon content distribution data of the hydrocarbon source rock of the target interval in combination with the relationship model and the P-wave impedance body; a hydrocarbon source rock thickness distribution module, which obtains hydrocarbon source rock thickness distribution data of the target interval in combination with the P-wave impedance body and the P-wave hydrocarbon source rock impedance threshold.

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