Method for predicting oil resource quantity of thin-well low-exploration basin
By determining the hydrocarbon generation threshold depth and hydrocarbon generation period in sparsely explored basins, reconstructing paleotectonic structures, delineating the area of mature source rocks, calibrating the distribution of dark mudstone, and calculating the mudstone percentage content and thickness model, the accuracy and efficiency issues of oil resource prediction in sparsely explored basins have been resolved, and more accurate resource calculations have been achieved.
Patent Information
- Application Number
- CN202510977434.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-16
- Publication Date
- 2025-10-31
AI Technical Summary
In sparsely explored basins, existing technologies struggle to accurately predict oil resources, primarily due to a lack of sufficient drilling data and geological information. This leads to an inconsistency in determining the thickness and distribution area of source rocks, resulting in high uncertainty in the prediction results.
By determining the hydrocarbon generation threshold depth and generation period of the source rock strata, paleotectonic structures are reconstructed, the area of mature source rocks is delineated, the distribution range of dark mudstone is marked, the mudstone percentage content and thickness model are calculated, and the amount of oil resources is calculated using formulas.
It improves the accuracy of source rock area and thickness data, simplifies calculation complexity, and enhances the accuracy and efficiency of oil resource prediction.
Smart Images
Figure CN120873344A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of oil and gas exploration and development technology. More specifically, this invention relates to a method for predicting oil resources in sparsely populated, low-exploration basins. Background Technology
[0002] Oil and gas resource prediction is an important component of comprehensive petroleum geology research. Various methods are based on different principles and are applicable to different exploration stages and geological conditions. Different methods are used at different exploration stages and for different exploration objects, such as genetic methods, volumetric methods, analogical methods, and statistical methods.
[0003] Genetic methods predict oil and gas resources by calculating hydrocarbon generation, migration, accumulation, and preservation based on geological processes. Volumetric methods estimate resources by measuring reservoir volume and related parameters. Analogy methods infer oil and gas resource potential by comparing a target basin with known similar basins. Statistical methods rely on data from discovered oil and gas reservoirs, predicting resources by analyzing the statistical relationships between resource abundance and geological parameters.
[0004] Sparse-well, low-exploration basins refer to sedimentary basins with very few wells and low exploration levels. Predicting oil resources in these basins is a major challenge in oil and gas exploration. Conventional resource prediction methods rely on key parameters such as source rock thickness, organic carbon content, and maturity. However, due to a lack of sufficient drilling data and geological data, the application of conventional resource prediction methods is greatly limited, and the prediction results are subject to significant uncertainty. The main reasons for this are:
[0005] Under sparse well conditions, the limited number of wells cannot represent the overall characteristics of the basin, making it difficult to characterize the spatial distribution patterns of source rocks. Low-exploration areas often experience multiple phases of tectonic movement, making it difficult to accurately characterize the contact relationship between faults and strata, affecting the assessment of hydrocarbon-generating depression boundaries and resource scale; it is also difficult to distinguish the thickness differences of source rock layers in different tectonic units. Furthermore, it is difficult to rule out the interference caused by different types of lithological materials in the source rock layers on thickness and area. Therefore, in the low-exploration stage, the values of the two key parameters—effective source rock thickness and distribution area—lack sufficient rationality and data support, leading to increased uncertainty in prediction results.
[0006] In view of this, there is an urgent need to provide a method for predicting the oil resources in sparsely explored basins, so as to provide accurate area and thickness data and improve the computational efficiency and accuracy of the prediction process. Summary of the Invention
[0007] To address at least one or more of the technical problems mentioned above, this invention provides a method for predicting oil resources in sparsely explored basins, comprising: a first step, determining the hydrocarbon generation threshold depth and hydrocarbon generation period of the source rock strata based on the source rock information obtained from drilling; a second step, reconstructing the paleotectonic structure of the source rock strata during the hydrocarbon generation period, and delineating the mature source rock area on the paleotectonic planar distribution map based on the hydrocarbon generation threshold depth; a third step, determining the distribution range of dark mudstone within the mature source rock area in the source rock strata to obtain the effective source rock area; a fourth step, obtaining the seismic strata velocity of the source rock strata, using sonic logging data and well logging data to calculate the pure mudstone velocity and pure sandstone velocity of the source rock strata, and substituting them into equation (I) to calculate the mudstone percentage content, wherein, equation (I): V int V represents the velocity of the seismic layer. m V represents the velocity of pure mudstone. s P represents the velocity of pure sandstone. m The fifth step is to obtain the thickness model of the source rock strata and construct the mudstone thickness model of the source rock strata based on the mudstone percentage content. The sixth step is to divide the mudstone thickness model within the effective source rock area into multiple units and calculate the average thickness of each unit. The seventh step is to obtain the density, organic matter conversion coefficient, organic carbon content and aggregation coefficient of the source rock, calculate the oil resource quantity of each unit using formula (II), and sum them up to obtain the basin oil resource quantity, where formula (II) is: Q1 represents the basin's oil resources, n represents the total number of units, and i ranges from 1 to n to represent the unit number. si H represents the effective source rock area of the i-th unit. si Let ρ represent the average thickness of the i-th unit, ρ represent the density of the source rock, C represent the organic carbon content, and K represent the average thickness of the i-th unit. c K represents the organic matter conversion coefficient. a This represents the clustering coefficient.
[0008] According to one embodiment of the present invention, in the first step, the hydrocarbon generation threshold depth of the source rock section is determined based on the geochemical properties of the source rock sample obtained by drilling; the stratigraphic age corresponding to the hydrocarbon generation threshold depth in the drilling is determined as the hydrocarbon generation period; the source rock information includes at least: pyrolysis parameters, vitrinite reflectance, and organic matter composition.
[0009] According to one embodiment of the present invention, in the second step, on the morphological planar distribution map of the paleotectonic structure, contour lines of the hydrocarbon generation threshold depth are taken, and the portion below the hydrocarbon generation threshold depth is delineated as the area of mature hydrocarbon source rock.
[0010] According to one embodiment of the present invention, in the third step, a seismic facies type planar distribution map of the source rock strata is obtained. Using the organic facies characteristics and types of the source rock obtained from drilling, dark mudstone is marked on the seismic facies type planar distribution map to determine the distribution range of dark mudstone.
[0011] According to one embodiment of the present invention, in the fourth step, the seismic velocity of the source rock section is calculated in units of an integer multiple of the size of the seismic survey grid.
[0012] According to one embodiment of the present invention, in the fourth step, the pure mudstone velocity and pure sandstone velocity of the source rock stratum are determined by the following method: interpreting the sonic transit time of pure mudstone and pure sandstone using sonic logging data and well logging data; fitting a polynomial relating pure mudstone velocity to burial depth; fitting a polynomial relating pure sandstone velocity to burial depth; obtaining the burial depth of the source rock stratum, and calculating the pure mudstone velocity and pure sandstone velocity of the source rock stratum; wherein, the burial depth of the source rock stratum is taken as the burial depth of the top interface, bottom interface, or the midpoint between the top and bottom interfaces of the source rock.
[0013] According to one embodiment of the present invention, in the fifth step, the seismic two-way travel time of the top and bottom interfaces of the source rock section is picked up, and the thickness of the source rock section is calculated using time-depth relationship data to obtain a thickness model of the source rock section; the thickness in the thickness model of the source rock section is multiplied by the mudstone percentage to obtain a mudstone thickness model of the source rock section.
[0014] According to one embodiment of the present invention, in the sixth step, the unit is divided according to the depth contour lines or the unit is divided according to the size of the seismic survey line grid.
[0015] According to another aspect of the present invention, a method for predicting the oil resources in a sparsely populated, low-exploration basin is provided, comprising: a first step of obtaining the seismic velocity of the source rock strata, determining the pure mudstone velocity and pure sandstone velocity of the source rock strata using sonic logging data and well logging data, and substituting them into equation (Ⅰ) to calculate the mudstone percentage content, wherein, equation (Ⅰ): V int V represents the velocity of the seismic layer. m V represents the velocity of pure mudstone. s P represents the velocity of pure sandstone. m The first step is to represent the percentage content of mudstone; the second step is to obtain the thickness model of the source rock strata, and construct the mudstone thickness model of the source rock strata based on the percentage content of mudstone; the third step is to obtain the density, area, organic matter conversion coefficient, organic carbon content and aggregation coefficient of the source rock, and calculate the oil resources of the basin using formula (II), where formula (II): Q1 represents the amount of oil resources in the basin, A s1 H represents the area of the source rock. s1dA represents the thickness of the mudstone, dH represents the differential unit of the source rock area, ρ represents the density of the source rock, C represents the organic carbon content, and K represents the density of the source rock. c K represents the organic matter conversion coefficient. a This represents the clustering coefficient.
[0016] According to another aspect of the present invention, a method for predicting the oil resources of a sparsely explored basin is provided, comprising: a first step, determining the hydrocarbon generation threshold depth and hydrocarbon generation period of the source rock strata based on the source rock information obtained from drilling; a second step, reconstructing the paleotectonic structure of the source rock strata during the hydrocarbon generation period, and delineating the area of mature source rock based on the hydrocarbon generation threshold depth on a planar distribution map of the paleotectonic structure; a third step, determining the distribution range of dark mudstone within the mature source rock area in the source rock strata to obtain the effective source rock area; and a fourth step, obtaining the density of the source rock, the mudstone thickness of the source rock strata, the organic matter conversion coefficient, the organic carbon content, and the aggregation coefficient, and calculating the oil resources of the basin using formula (II), wherein, formula (II): Q2 represents the oil resources in the basin, A s2 H represents the effective source rock area. s2 dA represents the thickness of the mudstone, dH represents the differential unit of the effective source rock area, ρ represents the density of the source rock, C represents the organic carbon content, and K represents the density of the source rock. c K represents the organic matter conversion coefficient. a This represents the clustering coefficient.
[0017] In this invention, the accuracy of source rock area delineation is improved by delineating the area of mature source rocks based on paleotectonic structures from the hydrocarbon generation period. By calibrating the area of dark mudstone, interference from non-source rocks is eliminated, further providing accurate area data for resource calculation. Calculating the percentage content of mudstone eliminates interference from non-source rocks in terms of thickness, providing accurate thickness data for resource calculation. Calculating resource quantity by dividing the rock into units simplifies computational complexity and improves computational efficiency and accuracy. Attached Figure Description
[0018] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein:
[0019] Figure 1 A schematic diagram illustrating the steps of a method for predicting oil resources in sparsely populated, low-exploration basins is shown. Detailed Implementation
[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0021] It should be understood that the terms "comprising" and "including" as used in the specification and claims of this invention indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.
[0022] It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this specification and claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations.
[0023] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
[0024] Figure 1 A schematic diagram illustrating the steps of a method for predicting oil resources in sparsely populated, low-exploration basins is shown.
[0025] like Figure 1 As shown, a method for predicting oil resources in sparsely explored basins includes: Step S1, determining the hydrocarbon generation threshold depth and hydrocarbon generation period of the source rock strata based on the source rock information obtained from drilling; Step S2, reconstructing the paleotectonic structure of the source rock strata during the hydrocarbon generation period, and delineating the mature source rock area based on the hydrocarbon generation threshold depth on the planar distribution map of the paleotectonic structure; Step S3, determining the distribution range of dark mudstone within the mature source rock area in the source rock strata to obtain the effective source rock area; Step S4, obtaining the seismic strata velocity of the source rock strata, using sonic logging data and well logging data to calculate the pure mudstone velocity and pure sandstone velocity of the source rock strata, and substituting them into Equation (I) to calculate the mudstone percentage content, where Equation (I): V int V represents the velocity of the seismic layer. m V represents the velocity of pure mudstone. s P represents the velocity of pure sandstone. mThe percentage content of mudstone is indicated; in step S5, the thickness model of the source rock strata is obtained, and the mudstone thickness model of the source rock strata is constructed based on the percentage content of mudstone; in step S6, the mudstone thickness model of the source rock strata within the effective source rock area is divided into multiple units, and the average thickness of each unit is calculated; in step S7, the density, organic matter conversion coefficient, organic carbon content and aggregation coefficient of the source rock are obtained, and the oil resource quantity of each unit is calculated using formula (II). After summing, the oil resource quantity of the basin is obtained, where formula (II) is: Q1 represents the basin's oil resources, n represents the total number of units, and i ranges from 1 to n to represent the unit number. si H represents the effective source rock area of the i-th unit. si The thickness of the mudstone in the i-th unit is represented by ρ, the density of the source rock is represented by C, and the organic carbon content is represented by K. c K represents the organic matter conversion coefficient. a This represents the clustering coefficient.
[0026] In this invention, the methods for obtaining the effective hydrocarbon source rock area and the mudstone thickness of the hydrocarbon source rock strata have been improved, thereby enhancing the accuracy of both.
[0027] In this invention, before predicting oil resources, in sparsely explored basins, the basin's stratigraphy, structural information, two-dimensional or three-dimensional seismic data, and seismic velocity spectrum are obtained through seismic data, and well logging, well logging, and source rock geochemical data of wells in the region are collected.
[0028] Based on drilling data, key source rock information related to the hydrocarbon generation threshold can be obtained. This source rock information includes at least: pyrolysis parameters, vitrinite reflectance, and organic matter composition. This information can then be used to determine the hydrocarbon generation potential and thermal evolution stage of the source rock, thereby determining the hydrocarbon generation threshold depth.
[0029] Specifically, in the first step S1, the hydrocarbon generation threshold depth mainly depends on indicators such as vitrinite reflectance, pyrolysis parameters, and geochemical profiles. Hydrocarbon source rock samples are obtained through sparse drilling, and the hydrocarbon generation threshold depth and generation period can be accurately characterized by integrating multiple parameters including geochemistry, well logging, and pressure testing. Vitrinite reflectance is obtained through drilling core or cuttings core analysis. Total organic carbon content, soluble hydrocarbon content, and pyrolytic hydrocarbon content, among other rock pyrolysis parameters, can be obtained through rock pyrolysis experiments or well logging curves. Organic matter conversion efficiency can be obtained through rock sample solvent extraction experiments. Well logging curve parameters include sonic transit time and resistivity, as well as density and porosity curves. Based on the above source rock information, those skilled in the art can comprehensively determine the hydrocarbon generation threshold depth of the source rock strata. The determination process is prior art and will not be elaborated upon in this invention.
[0030] Drilling is used to obtain the stratigraphic age of each stratum. After determining the hydrocarbon generation threshold depth, the stratigraphic age corresponding to the hydrocarbon generation threshold depth in the well is determined, which is the hydrocarbon generation period.
[0031] In the second step S2, based on existing seismic data, paleotectonic restoration methods, such as paleostress thickness restoration, erosion thickness restoration, and paleofault displacement restoration, are used to reconstruct the paleotectonic structure during the hydrocarbon generation period. This includes using seismic profile amplitude reflection characteristics and well logging data to determine the stratigraphic interfaces of the key hydrocarbon generation period, using layer flattening techniques to flatten the paleogeographic top boundary strata to the hydrocarbon generation period, and reconstructing the paleogeographic bottom boundary morphology formed during the erosion period. The obtained paleotectonic structure includes three dimensions: the planar distribution of the paleotectonic structure and the depth indicated by contour lines.
[0032] On the planar distribution map of paleotectonic morphology during the hydrocarbon generation period, contour lines corresponding to the hydrocarbon generation threshold depth are drawn, and the area below the hydrocarbon generation threshold depth is delineated as the area of mature source rocks. Within the mature source rock area, there are other types of blocks, such as sandstone, that do not possess hydrocarbon generation structures, and these need to be excluded to accurately determine the effective source rock area.
[0033] In step S3, dark mudstone refers to fine-grained sedimentary rocks rich in organic matter, with a black or grayish-black color, reflecting a strongly reducing sedimentary environment. Its organic carbon content is typically higher than 0.5%, making it a major source rock type in continental basins. The thickness and distribution range of dark mudstone directly affect its hydrocarbon generation potential. The effective source rock area refers to the continuous distribution area of dark mudstone within the mature source rock range that simultaneously meets the requirements for organic matter abundance, type, and hydrocarbon expulsion conditions; it forms the basis for calculating petroleum resources.
[0034] In this invention, the distribution of dark mudstone can be identified by seismic inversion, and the planar distribution of dark mudstone can be constrained by the organic phase characteristics and types of source rocks obtained from drilling.
[0035] Specifically, the process involves obtaining a planar distribution map of seismic facies types within the source rock strata, calibrating the seismic facies, and determining the distribution range of dark mudstone. This includes converting the acquired seismic data from the time domain to the frequency domain and determining a constant bandwidth. Seismic facies are then classified based on waveform similarity, generating model traces and calculating inter-class spacing. The classification results are mapped onto a plane, and after calibration and correction using data such as drilling data, a planar distribution map of the seismic facies is finally drawn, revealing the distribution range of dark mudstone.
[0036] In this invention, the area of mature source rocks is delineated based on paleotectonic structures from the hydrocarbon generation period. This method offers several advantages over delineating mature source rocks based on current structures: paleotectonic structures from the hydrocarbon generation period can reconstruct the burial depth and temperature conditions of the source rocks during key hydrocarbon generation stages, avoiding misjudgments of maturity due to later tectonic uplift or erosion; it can prevent misclassification of later tectonic high areas as immature zones; and it can clearly define the matching relationship between hydrocarbon migration and accumulation pathways, reducing interference from later tectonic alterations. This invention significantly improves the accuracy of mature source rock area delineation, providing accurate area data for resource calculation.
[0037] Determining the distribution range of dark mudstone within the area of mature source rocks can identify high-quality source rocks, eliminate interference from non-source rocks, and provide more accurate area data for resource calculation.
[0038] In step S4, seismic layer velocity refers to the average interlayer velocity of seismic waves propagating in a specific stratum. Its physical meaning is the reciprocal of the time required for a P-wave to traverse a unit thickness of stratum, directly reflecting the lithology, porosity, and fluid properties of the strata. Pure mudstone velocity and pure sandstone velocity represent the seismic wave propagation velocities of mudstone sections without sandstone interlayers and sandstone sections without argillaceous interlayers, respectively.
[0039] Seismic layer velocities are typically not fixed values, but rather parameters that vary with lithology, depth, and geological conditions. Because the proportions of mudstone and sandstone within source rock strata are not uniform, the seismic layer velocities differ at different points on the horizontal coordinate system, thus forming a seismic layer velocity model for source rock strata. The observation scale for seismic layer velocities in source rock strata is set to an integer multiple of the seismic survey line grid size, and calculations are performed in units of integer multiples of the seismic survey line grid size.
[0040] Specifically, a seismic velocity distribution model for the oil-bearing zone is constructed: Two marker layers are selected in the shallow and intermediate layers of the basin. High-precision inter-marker velocity data are obtained using depth data between the two marker layers obtained from drilled wells and the two-way travel time between the two marker layers after well-seismic calibration. For the interface between the two marker layers in the middle and shallow layers, the stacking velocity of the seismic velocity spectrum is picked up. After stacking velocity dip correction, the root mean square velocity is obtained. Then, the seismic velocity between the two marker layers is calculated using the DIX formula. Using the inter-marker velocity data in the middle and shallow layers, systematic error correction is performed on the corresponding seismic velocity to obtain the seismic velocity error correction amount.
[0041] Seismic velocity spectra were interpreted at the top and bottom interfaces of the source rock strata to obtain the seismic stacking velocity. Using a seismic dip correction formula, the seismic stacking velocity was dip-corrected to obtain the root-mean-square (RMS) velocities at the top and bottom interfaces of the source rock strata. Using the DIX formula, based on the RMS velocities at the top and bottom interfaces of the source rock strata and their corresponding two-way travel times, the seismic layer velocity of the source rock strata was obtained.
[0042] By using the error correction amount of shallow and intermediate seismic layer velocity, a systematic error correction is performed on the seismic layer velocity of the source rock section to obtain the seismic layer velocity of the source rock section with improved accuracy. Then, by modeling the velocity data of this layer, a seismic layer velocity distribution model of the source rock section is formed.
[0043] The velocities of pure mudstone and pure sandstone in the source rock strata are obtained using the following methods: acoustic logging and well logging data are used to interpret the acoustic transit time of pure mudstone and pure sandstone; a polynomial is fitted to represent the relationship between pure mudstone velocity and burial depth; a polynomial is also fitted to represent the relationship between pure sandstone velocity and burial depth; that is, a function is constructed with burial depth as the independent variable to express the dependent variables of pure sandstone velocity and pure mudstone velocity. After obtaining the burial depth of the source rock strata, the burial depth is substituted into the above function to calculate the pure mudstone velocity and pure sandstone velocity of the source rock strata. The burial depth of the source rock strata is calculated using the top interface, bottom interface, or the midpoint between the top and bottom interfaces. Preferably, the burial depth of the source rock is calculated using the midpoint between the top and bottom interfaces.
[0044] The seismic layer velocity, pure mudstone velocity, and pure sandstone velocity are substituted into the time-averaged equation (I) to calculate the mudstone percentage content. Wherein, equation (I): V int V represents the velocity of the seismic layer. m V represents the velocity of pure mudstone. s P represents the velocity of pure sandstone. m This indicates the percentage content of mudstone.
[0045] The mudstone percentage data from the source rock strata constitute multiple numerical points corresponding to planar coordinates. The density of these data points is the same as that of the seismic layer velocity data.
[0046] In step S5, the two-way seismic travel time at the top and bottom interfaces of the source rock section is picked up. Using the time-depth relationship data, the thickness of the source rock section is calculated, resulting in a source rock section thickness model. The thickness of the source rock section in the source rock section thickness model is then multiplied by the mudstone percentage content to obtain the mudstone thickness model of the source rock section. The source rock section thickness corresponds to the mudstone percentage data; the average thickness of the area corresponding to each mudstone percentage data is calculated based on the density of the mudstone percentage data, and then multiplied for calculation.
[0047] In this invention, the mudstone thickness percentage of the source rock strata is calculated, and based on this, a mudstone thickness model for the source rock strata is derived. This eliminates interference from non-source rocks in terms of thickness, providing accurate thickness data for resource calculation.
[0048] In step S6, the elements are divided according to depth contour lines or according to the size of the seismic survey grid. To improve computational efficiency, this invention divides the source rock strata into elements to simplify the calculation. Depth contour lines refer to the depth contour lines on the structural plan within the effective source rock area. This division allows areas of similar thickness to be grouped into the same element, improving calculation accuracy. The seismic survey grid consists of mutually perpendicular or oblique excitation and reception lines, forming regular grid elements or facets. This division allows for control of the element size, improving computational efficiency.
[0049] In step S7, the density and organic carbon content of the source rock are obtained through well logging, while the organic matter conversion coefficient and aggregation coefficient are obtained through regional statistics. These parameters can be obtained using existing methods, and this invention is not limited to any particular method. For example, the density of the source rock can be directly measured by drilling source rock cores using a helium porosimeter or saturated water weighing method. The organic matter conversion coefficient is obtained by simulating the hydrocarbon generation process of the source rock through gold tube pyrolysis experiments, determining the hydrocarbon production rate at different maturity levels, and establishing a relationship curve. The organic carbon content (TOC) and soluble organic matter content are directly determined from the obtained core samples. The aggregation coefficient is obtained by referencing the hydrocarbon accumulation efficiency of similar basins using geostatistical methods.
[0050] After obtaining the above parameters, substitute them into the resource quantity calculation formula (II) to obtain the resource quantity of each unit, and then sum them to obtain the oil resource quantity of the basin.
[0051] Among them, equation (II): Q1 represents the basin's oil resources, n represents the total number of subdivisions, and i ranges from 1 to n, where i represents the subdivision number. si H represents the effective source rock area of the i-th unit. si The thickness of the mudstone in the i-th unit is represented by ρ, the density of the source rock is represented by C, and the organic carbon content is represented by K. c K represents the organic matter conversion coefficient. a This represents the clustering coefficient.
[0052] This invention also provides another method for predicting oil resources in sparsely populated, low-exploration basins, comprising: a first step, obtaining the seismic velocity of the source rock strata, using sonic logging data and well logging data to determine the pure mudstone velocity and pure sandstone velocity of the source rock strata, and substituting them into equation (Ⅰ) to calculate the mudstone percentage content, wherein, equation (Ⅰ): V int V represents the velocity of the seismic layer. m V represents the velocity of pure mudstone. s P represents the velocity of pure sandstone. mThe first step is to represent the percentage content of mudstone; the second step is to obtain the thickness model of the source rock strata, and construct the mudstone thickness model of the source rock strata based on the percentage content of mudstone; the third step is to obtain the density, area, organic matter conversion coefficient, organic carbon content and aggregation coefficient of the source rock, and calculate the oil resources of the basin using formula (II), where formula (II): Q1 represents the amount of oil resources in the basin, A s1 H represents the area of the source rock. s1 dA represents the thickness of the mudstone, dH represents the differential unit of the source rock area, ρ represents the density of the source rock, C represents the organic carbon content, and K represents the density of the source rock. c K represents the organic matter conversion coefficient. a This represents the clustering coefficient.
[0053] That is, the difference from the first scheme is that it only improves the accuracy of mudstone thickness in the thickness dimension, without involving improvements in the accuracy of source rock area or improvements in computational efficiency through cell partitioning.
[0054] This invention also provides another method for predicting the oil resources in sparsely explored basins, comprising: a first step, determining the hydrocarbon generation threshold depth and hydrocarbon generation period of the source rock strata based on the source rock information obtained from drilling; a second step, reconstructing the paleotectonic structure of the source rock strata during the hydrocarbon generation period, and delineating the area of mature source rocks based on the hydrocarbon generation threshold depth on the planar distribution map of the paleotectonic structure; a third step, determining the distribution range of dark mudstone within the mature source rock area in the source rock strata to obtain the effective source rock area; and a fourth step, obtaining the density of the source rocks, the mudstone thickness of the source rock strata, the organic matter conversion coefficient, the organic carbon content, and the aggregation coefficient, and calculating the oil resources of the basin using formula (II).
[0055] Among them, equation (II): Q2 represents the oil resources in the basin, A s2 H represents the effective source rock area. s2 dA represents the thickness of the mudstone, dH represents the differential unit of the effective source rock area, ρ represents the density of the source rock, C represents the organic carbon content, and K represents the density of the source rock. c K represents the organic matter conversion coefficient. a This represents the clustering coefficient.
[0056] That is, the difference from the first scheme is that it only provides a more accurate effective source rock area, without involving improvements in the accuracy of mudstone thickness in the thickness dimension or improvements in computational efficiency through cell partitioning.
[0057] In this invention, the accuracy of source rock area delineation is improved by delineating the area of mature source rocks based on paleotectonic structures from the hydrocarbon generation period. By calibrating the area of dark mudstone, interference from non-source rocks is eliminated, further providing accurate area data for resource calculation. Calculating the percentage content of mudstone eliminates interference from non-source rocks in terms of thickness, providing accurate thickness data for resource calculation. Calculating resource quantity by dividing the rock into units simplifies computational complexity and improves computational efficiency and accuracy.
[0058] While numerous embodiments of the invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of the invention. The appended claims are intended to define the scope of protection of the invention and therefore cover equivalents or alternatives within the scope of these claims.
Claims
1. A method for predicting oil resources in sparsely populated, low-exploration basins, characterized in that, include: The first step is to determine the hydrocarbon generation threshold depth and hydrocarbon generation period of the source rock strata based on the source rock information obtained from drilling. The second step is to reconstruct the paleotectonic structure of the source rock strata during the hydrocarbon generation period, and delineate the area of mature source rock on the planar distribution map of the paleotectonic structure based on the hydrocarbon generation threshold depth. The third step is to determine the distribution range of dark mudstone within the mature source rock area in the source rock strata, thereby obtaining the effective source rock area. The fourth step is to obtain the seismic velocities of the source rock strata, and use sonic logging and well logging data to determine the pure mudstone and pure sandstone velocities of the source rock strata. These velocities are then substituted into equation (Ⅰ) to calculate the mudstone percentage. Wherein, equation (Ⅰ): V int V represents the velocity of the seismic layer. m V represents the velocity of pure mudstone. s P represents the velocity of pure sandstone. m Indicates the percentage content of mudstone; The fifth step is to obtain the thickness model of the source rock strata and construct the mudstone thickness model of the source rock strata based on the mudstone percentage content. The sixth step is to divide the mudstone thickness model within the effective source rock area into multiple units and calculate the average thickness of each unit. The seventh step involves obtaining the density, organic matter conversion coefficient, organic carbon content, and aggregation coefficient of the source rock. Using equation (II), the oil resource quantity of each unit is calculated, and the results are summed to obtain the total oil resource quantity of the basin. Among them, equation (II): Q1 represents the basin's oil resources, n represents the total number of units, and i ranges from 1 to n to represent the unit number. si H represents the effective source rock area of the i-th unit. si Let ρ represent the average thickness of the i-th unit, ρ represent the density of the source rock, C represent the organic carbon content, and K represent the average thickness of the i-th unit. c K represents the organic matter conversion coefficient. a This represents the clustering coefficient.
2. The method according to claim 1, characterized in that, In the first step, the hydrocarbon generation threshold depth of the source rock interval is determined based on the geochemical properties of the source rock samples obtained through drilling. The formation age corresponding to the hydrocarbon generation threshold depth in the well is determined as the hydrocarbon generation period; The source rock information includes at least: pyrolysis parameters, vitrinite reflectance, and organic matter composition.
3. The method according to claim 1, characterized in that, In the second step, on the morphological planar distribution map of the ancient structure, the contour line of the hydrocarbon generation threshold depth is taken, and the area below the hydrocarbon generation threshold depth is delineated as the area of mature source rock.
4. The method according to claim 1, characterized in that, In the third step, a seismic facies type planar distribution map of the source rock strata is obtained. Using the organic facies characteristics and types of the source rock obtained from drilling, dark mudstone is marked on the seismic facies type planar distribution map to determine the distribution range of dark mudstone.
5. The method according to claim 1, characterized in that, In the fourth step, the seismic velocity of the source rock strata is calculated in integer multiples of the size of the seismic survey grid.
6. The method according to claim 1, characterized in that, In the fourth step, the pure mudstone velocity and pure sandstone velocity of the source rock strata are determined by the following method: Using sonic logging and well logging data, the sonic transit time of pure mudstone and pure sandstone was interpreted; Fit a polynomial to the relationship between velocity and burial depth in pure mudstone; Fit a polynomial to the relationship between velocity and burial depth in pure sandstone; Obtain the burial depth of the source rock strata and calculate the pure mudstone velocity and pure sandstone velocity of the source rock strata. The burial depth of the source rock section is determined by the burial depth of the top interface, bottom interface, or midpoint between the top and bottom interfaces of the source rock.
7. The method according to claim 1, characterized in that, In the fifth step, the seismic two-way time of the top and bottom interfaces of the source rock section is picked up, and the thickness of the source rock section is calculated using the time-depth relationship data to obtain the thickness model of the source rock section. The thickness of the source rock strata is obtained by multiplying the thickness in the thickness model with the mudstone percentage.
8. The method according to claim 1, characterized in that, In the sixth step, the unit is divided according to depth contour lines or according to the size of the seismic survey line grid.
9. A method for predicting oil resources in sparsely populated, low-exploration basins, characterized in that, include: The first step is to obtain the seismic strata velocity of the source rock strata, and then use sonic logging and well logging data to determine the pure mudstone and pure sandstone velocities of the source rock strata. These velocities are then substituted into equation (Ⅰ) to calculate the mudstone percentage. Wherein, equation (Ⅰ): V int V represents the velocity of the seismic layer. m V represents the velocity of pure mudstone. s P represents the velocity of pure sandstone. m Indicates the percentage content of mudstone; The second step is to obtain the thickness model of the source rock strata and construct the mudstone thickness model of the source rock strata based on the mudstone percentage content. The third step involves obtaining the density, area, organic matter conversion coefficient, organic carbon content, and aggregation coefficient of the source rock, and then using formula (II) to calculate the basin's oil resources. Among them, equation (II): Q1 represents the amount of oil resources in the basin, A s1 H represents the area of the source rock. s1 dA represents the thickness of the mudstone, dH represents the differential unit of the source rock area, ρ represents the density of the source rock, C represents the organic carbon content, and K represents the density of the source rock. c K represents the organic matter conversion coefficient. a This represents the clustering coefficient.
10. A method for predicting oil resources in sparsely populated, low-exploration basins, characterized in that, include: The first step is to determine the hydrocarbon generation threshold depth and hydrocarbon generation period of the source rock strata based on the source rock information obtained from drilling. The second step is to reconstruct the paleotectonic structure of the source rock strata during the hydrocarbon generation period, and delineate the area of mature source rock on the planar distribution map of the paleotectonic structure based on the hydrocarbon generation threshold depth. The third step is to determine the distribution range of dark mudstone within the mature source rock area in the source rock strata, thereby obtaining the effective source rock area. The fourth step involves obtaining the density of the source rock, the mudstone thickness of the source rock strata, the organic matter conversion coefficient, the organic carbon content, and the aggregation coefficient. Then, formula (II) is used to calculate the basin's oil resources. Among them, equation (II): Q2 represents the oil resources in the basin, A s2 H represents the effective source rock area. s2 dA represents the thickness of the mudstone, dH represents the differential unit of the effective source rock area, ρ represents the density of the source rock, C represents the organic carbon content, and K represents the density of the source rock. c K represents the organic matter conversion coefficient. a This represents the clustering coefficient.