Method for predicting thickness of dark mudstone in thin-well low-exploration basin
By obtaining seismic layer velocity and sonic logging data, combining them with seismic facies type maps to determine the range of dark mudstone, and constructing a mudstone thickness model, the problem of accurate prediction of dark mudstone thickness in sparsely welled and poorly explored basins was solved, thereby improving the accuracy of oil resource prediction.
Patent Information
- Application Number
- CN202510977432.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-16
- Publication Date
- 2025-10-21
AI Technical Summary
In basins with sparse wells and low exploration, existing technologies have difficulty in accurately predicting the thickness and distribution of dark mudstone, resulting in high uncertainty in the prediction results of oil resources.
By obtaining the seismic layer velocity and thickness model of the source rock interval, the mudstone percentage is calculated using acoustic logging and mud recording data. The dark mudstone range is determined by combining the seismic facies type plane distribution map, a dark mudstone thickness model is constructed, and the thickness is calculated based on the unit division.
The accuracy of dark mudstone thickness and distribution is improved, calculation uncertainty is reduced, and the accuracy of oil resource prediction is improved.
Smart Images
Figure CN120822345A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of oil and gas exploration and development. More specifically, the present invention relates to a method for predicting the thickness of dark mudstone in a basin with sparse wells and low exploration. Background Art
[0002] Oil and gas resource prediction is an essential 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 targets, such as genetic methods, volumetric methods, analogical methods, and statistical methods.
[0003] The genetic approach predicts oil and gas resources by calculating hydrocarbon generation based on the geological processes of oil and gas generation, migration, accumulation, and preservation. The volumetric approach calculates resource abundance by estimating reservoir volume and related parameters. The analogical approach infers oil and gas resource potential by comparing a target basin with known similar basins. The statistical approach relies on data from discovered reservoirs and predicts resource abundance by analyzing the statistical relationship between resource abundance and geological parameters.
[0004] Sparsely drilled, low-exploration basins are sedimentary basins with very few wells and a low degree of exploration. Predicting oil resources in these basins is a 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 and geological data, their application is greatly limited, and the prediction results are subject to significant uncertainty. The main reasons are:
[0005] Under sparse well conditions, the few wells drilled cannot fully characterize the basin's source rocks, making it difficult to delineate their spatial distribution patterns. In low-exploration areas, it's difficult to distinguish thickness differences between source rock layers within different tectonic units, and it's difficult to rule out the influence of different lithologic types on the overall thickness. Furthermore, during the early exploration phase, direct prediction of mudstone color and its distribution using seismic data is difficult. Consequently, determining the thickness and distribution of dark mudstone, two key parameters, lacks sufficient rationality and data support, leading to increased uncertainty in predictions.
[0006] In view of this, there is an urgent need to provide a method for predicting the thickness of dark mudstone in sparsely welled and poorly explored basins, so as to provide accurate thickness data and improve the accuracy of predicting oil resources. Summary of the Invention
[0007] In order to at least solve one or more of the technical problems mentioned above, the present invention provides a method for predicting the thickness of dark mudstone in a sparsely welled and underexplored basin, comprising: a first step of obtaining the seismic interval velocity of a source rock interval and a thickness model of the source rock interval; a second step of calculating the pure mudstone velocity and pure sandstone velocity of the source rock interval using sonic logging data and mud logging data; and a third step of substituting the seismic interval velocity, the pure mudstone velocity, and the pure sandstone velocity of the source rock interval into a calculation formula (I) to calculate the mudstone percentage, wherein formula (I) is: V int represents the seismic layer velocity, V m represents the velocity of pure mudstone, V s represents the pure sandstone velocity, P m Indicates the percentage of mudstone; the fourth step is to obtain a plane distribution map of the seismic facies type of the source rock segment, and use the organic facies characteristics and types of the source rock obtained by drilling to calibrate the dark mudstone in the plane distribution map of the seismic facies type and determine the distribution range of the dark mudstone; the fifth step is to construct a dark mudstone thickness model within the distribution range of the dark mudstone based on the thickness model and the mudstone percentage.
[0008] According to one embodiment of the present invention, in the first step, the seismic two-way time of the top and bottom interfaces of the source rock layer is picked up, and the thickness of the source rock layer is calculated using the time-depth relationship data to obtain the source rock layer thickness model.
[0009] According to one embodiment of the present invention, in the second step, the pure mudstone velocity and pure sandstone velocity of the source rock layer section are obtained by the following method: using sonic logging data and mud logging data to interpret the sonic time difference of pure mudstone and pure sandstone; fitting the changing relationship between the pure mudstone velocity and the burial depth; fitting the changing relationship between the pure sandstone velocity and the burial depth; obtaining the burial depth of the source rock layer section, and calculating the pure mudstone velocity and pure sandstone velocity of the source rock layer section.
[0010] According to one embodiment of the present invention, the burial depth of the source rock layer is the depth of the top interface, the bottom interface or the midpoint of the top interface and the bottom interface.
[0011] According to one embodiment of the present invention, the relationship between the velocity of pure mudstone and the burial depth is obtained by using any one of polynomial fitting, least squares fitting and piecewise linear fitting.
[0012] According to one embodiment of the present invention, in the fourth step, the source rock layer thickness in the source rock layer thickness model is multiplied by the mudstone percentage to obtain the mudstone thickness model of the source rock layer.
[0013] According to another aspect of the present invention, a method for predicting the thickness of dark mudstone in a sparsely welled and poorly explored basin is provided, comprising: a first step of obtaining a seismic interval velocity and a thickness model of a source rock interval; a second step of fitting a relationship between pure mudstone velocity and burial depth using acoustic logging data and mud logging data, and a third step of fitting a relationship between pure sandstone velocity and burial depth using acoustic logging data and mud logging data; a third step of vertically dividing the thickness model of the source rock interval into a plurality of units, and obtaining the seismic interval velocity, pure mudstone velocity, and pure sandstone velocity of the source rock interval in each unit; and a fourth step of substituting the seismic interval velocity, pure mudstone velocity, and pure sandstone velocity of the source rock interval in each unit into a calculation formula (I) to calculate the mudstone percentage of each unit, wherein formula (I) is: V int represents the seismic layer velocity, V m represents the velocity of pure mudstone, V s represents the pure sandstone velocity, P m Indicates the percentage of mudstone; the fifth step is to obtain a plane distribution map of the seismic facies type of the source rock segment, and use the organic facies characteristics and types of the source rock obtained by drilling to calibrate the dark mudstone in the plane distribution map of the seismic facies type and determine the distribution range of the dark mudstone; the sixth step is to construct a dark mudstone thickness model of the source rock segment within the distribution range of the dark mudstone based on the thickness model of the source rock segment and the mudstone percentage of each unit.
[0014] According to one embodiment of the present invention, the cells are divided into integer multiples of the seismic line grid size.
[0015] According to one embodiment of the present invention, the cells are divided according to depth contours.
[0016] According to one embodiment of the present invention, the mudstone thickness model of the source rock layer is obtained by multiplying the average thickness of each unit of the thickness model of the source rock layer by the mudstone percentage.
[0017] In this paper, by calculating the mudstone percentage, the interference of non-source rock in thickness is eliminated, providing accurate thickness data for resource calculation. By calculating thickness data by dividing the unit, accurate differentiated thickness data is further provided for resource calculation, improving computational efficiency and accuracy. By calibrating the dark mudstone seismic facies-constrained thickness model, the computational complexity of the dark mudstone thickness model can be reduced, providing accurate thickness data for petroleum resource calculation. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an illustrative and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:
[0019] Figure 1 A schematic diagram showing the steps of a method for predicting the thickness of dark mudstone in a sparsely welled and poorly explored basin. DETAILED DESCRIPTION
[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.
[0021] It should be understood that the terms "include" and "comprising" used in the description and claims of the present invention indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.
[0022] It should also be understood that the terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the present invention. As used in the 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 further be understood that the term "and / or" as used in the specification and claims refers to any and all possible combinations of one or more of the associated listed items, including and including these combinations.
[0023] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0024] Figure 1 A schematic diagram showing the steps of a method for predicting the thickness of dark mudstone in a sparsely welled and poorly explored basin.
[0025] like Figure 1 As shown, a method for predicting the thickness of dark mudstone in a sparsely welled and underexplored basin comprises: a first step S1, obtaining the seismic interval velocity of a source rock interval and a thickness model of the source rock interval; a second step S2, using acoustic logging data and mud logging data to obtain the pure mudstone velocity and pure sandstone velocity of the source rock interval; a third step S3, substituting the seismic interval velocity of the source rock interval, the pure mudstone velocity and the pure sandstone velocity of the source rock interval into a calculation formula (I) to calculate the mudstone percentage, wherein formula (I): V int represents the seismic layer velocity, V m represents the velocity of pure mudstone, V s represents the pure sandstone velocity, P m Indicates the percentage of mudstone; the fourth step S4, obtains the seismic facies type plane distribution map of the source rock layer, uses the organic facies characteristics and types of the source rock obtained by drilling, calibrates the dark mudstone in the seismic facies type plane distribution map, and determines the distribution range of the dark mudstone; the fifth step S5, within the distribution range of the dark mudstone, constructs a dark mudstone thickness model based on the thickness model and the mudstone percentage.
[0026] Seismic interval velocity refers to the average interlayer velocity of seismic waves propagating through a specific stratum. Its physical meaning is the inverse of the time required for a longitudinal wave to traverse a unit thickness of stratum. It directly reflects the lithology, porosity, and fluid properties of the stratum. Pure mudstone velocity and pure sandstone velocity represent the seismic wave propagation velocities in mudstone intervals without sandstone interlayers and sandstone intervals without argillaceous interlayers, respectively.
[0027] Seismic interval velocity is typically not a fixed value but a parameter that varies with formation lithology, depth, and geological conditions. Because the proportions of mudstone and sandstone within a source rock interval vary, the seismic interval velocity at each point on the horizontal coordinate varies, thus forming a seismic interval velocity model for the source rock interval. The observation scale for seismic interval velocity in the source rock interval is set to an integer multiple of the seismic line grid size.
[0028] Specifically, a seismic velocity distribution model for the oil-producing interval was constructed: two marker layers were selected in the shallow to mid-level basin. High-precision inter-layer velocity data were obtained using the depth segment data between the two marker layers acquired through drilling and the seismic two-way time difference between the two marker layers after well-seismic calibration. At the interface between the two marker layers in the shallow to mid-level basin, the stacking velocity of the seismic velocity spectrum was extracted. The root mean square velocity was then obtained by correcting the stacking velocity dip. The seismic interval velocity between the two marker layers was then calculated using the DIX formula. Using the inter-layer velocity data between the two marker layers in the shallow to mid-level basin, the corresponding seismic interval velocity was corrected for systematic errors, resulting in a seismic interval velocity error correction.
[0029] Seismic velocity spectra are interpreted for the top and bottom interfaces of the source rock interval to obtain seismic stacking velocities. Using the seismic dip correction formula, the seismic stacking velocities are dip-corrected to obtain the seismic root mean square (RMS) velocities at the top and bottom interfaces of the source rock interval. Using the DIX formula, the seismic layer velocity of the source rock interval is obtained based on the RMS velocities at the top and bottom interfaces of the source rock interval and their corresponding two-way time differences.
[0030] The seismic layer velocity of the source rock section is corrected for systematic errors using the mid-shallow seismic layer velocity error correction value to obtain the seismic layer velocity of the source rock section with improved accuracy. The seismic layer velocity distribution model of the source rock section is formed by modeling the velocity data of this layer.
[0031] The pure mudstone velocity and pure sandstone velocity of the source rock section are obtained by the following method: The pure mudstone velocity and pure sandstone velocity of the source rock section are determined by the following method: using sonic logging data and mud recording data to interpret the sonic time difference of pure mudstone and pure sandstone; fitting the changing relationship between pure mudstone velocity and burial depth; fitting the changing relationship between pure sandstone velocity and burial depth; obtaining the burial depth of the source rock section, and calculating the pure mudstone velocity and pure sandstone velocity of the source rock section. That is, using burial depth as the independent variable to construct a function to express the pure sandstone velocity and pure mudstone velocity, then obtaining the burial depth of the source rock section, substituting the burial depth into the above function to calculate the pure mudstone velocity and pure sandstone velocity of the source rock section; wherein the burial depth of the source rock section is the depth of the top interface, the bottom interface, or the midpoint of the top and bottom interfaces. Preferably, the burial depth of the source rock is the depth of the midpoint of the top and bottom interfaces. Preferably, the relationship between the velocity of pure mudstone and the burial depth is obtained by using any one of polynomial fitting, least squares fitting and piecewise linear fitting.
[0032] Substituting the seismic interval velocity, pure mudstone velocity, and pure sandstone velocity into the time-averaged equation of equation (I) calculates the mudstone percentage. The mudstone percentage data for the source rock interval form multiple numerical points corresponding to plane coordinates. The density of these data points is the same as the density of the seismic interval velocity data.
[0033] The seismic two-way time at the top and bottom interfaces of the source rock interval is collected and the thickness of the source rock interval is calculated using the time-depth relationship data to obtain a source rock interval thickness model. The source rock interval thickness in the source rock interval thickness model is then multiplied by the mudstone percentage to obtain a mudstone thickness model for the source rock interval. The source rock interval thickness corresponds to the mudstone percentage data, and the average thickness of the area corresponding to each mudstone percentage data is calculated based on the density of the mudstone percentage data for the product calculation.
[0034] In the present invention, seismic inversion can be used to identify mudstone distribution, and the organic phase characteristics and types of source rocks obtained by drilling can be combined to constrain the planar distribution of dark mudstone.
[0035] Specifically, a planar distribution map of seismic facies types within the source rock interval is obtained, and seismic facies are calibrated to determine the distribution range of dark mudstone. The acquired seismic data is converted from the time domain to the frequency domain, and a constant bandwidth is determined. Seismic facies are classified based on waveform similarity, model traces are generated, and inter-class spacing is calculated. The classification results are mapped onto a plane and, combined with calibration and correction methods such as drilling data, a planar distribution map of seismic facies is created to determine the distribution range of dark mudstone.
[0036] In this method, the mudstone thickness percentage of the source rock interval is calculated, and based on this, a mudstone thickness model for the source rock interval is derived. This eliminates interference from non-source rock in the thickness analysis, providing accurate thickness data for resource calculations. By calibrating the dark mudstone seismic facies-constrained thickness model, the computational complexity of the dark mudstone thickness model can be reduced, providing accurate thickness data for petroleum resource calculations.
[0037] According to another aspect of the present invention, a method for predicting the thickness of dark mudstone in a sparsely welled and poorly explored basin is provided, comprising: a first step of obtaining a seismic interval velocity and a thickness model of a source rock interval; a second step of fitting a relationship between pure mudstone velocity and burial depth and a relationship between pure sandstone velocity and burial depth using acoustic logging data and mud logging data; a third step of dividing the thickness model of the source rock interval vertically into a plurality of units, and obtaining the seismic interval velocity, pure mudstone velocity, and pure sandstone velocity of the source rock interval in each unit; and a fourth step of substituting the seismic interval velocity, pure mudstone velocity, and pure sandstone velocity of the source rock interval in each unit into formula (I) to calculate the mudstone percentage of each unit, wherein formula (I) is: V int represents the seismic layer velocity, V m represents the velocity of pure mudstone, V s represents the pure sandstone velocity, P m The fifth step is to obtain a seismic facies type planar distribution map of the source rock interval. Using the organic facies characteristics and types of the source rock obtained through drilling, dark mudstone is demarcated on the seismic facies type planar distribution map to determine the distribution range of the dark mudstone. The sixth step is to construct a dark mudstone thickness model for the source rock interval within the dark mudstone distribution range based on the source rock interval thickness model and the mudstone percentage of each unit.
[0038] The units are divided according to the depth contour lines, or the units are divided according to the size of the seismic survey grid. In order to improve the calculation efficiency, in the present invention, the source rock segment is divided into units, and the purpose of the division is to simplify the calculation complexity. Among them, the depth contour lines refer to the depth contour lines of the structural plane map within the effective source rock area. According to this division, areas with similar thickness can be divided into the same unit, thereby improving the accuracy of the calculation. The seismic survey grid is composed of mutually perpendicular or oblique excitation lines and receiving lines to form regular grid units or face elements. According to this division, the size of the unit can be controlled to improve the calculation efficiency. Preferably, the average thickness of each unit of the thickness model of the source rock segment is taken and the product is calculated by the percentage of mudstone.
[0039] The present invention also provides a method for predicting the amount of oil resources in a basin with sparse wells and low exploration, comprising: a first step of determining the hydrocarbon generation threshold depth and hydrocarbon generation period of the source rock interval based on source rock information obtained by drilling; a second step of restoring the paleostructure of the source rock interval during the hydrocarbon generation period, and delineating the mature source rock area based on the hydrocarbon generation threshold depth on a planar distribution map of the paleostructure; a third step of determining the distribution range of dark mudstone within the mature source rock area in the source rock interval to obtain the effective source rock area; and a fourth step of obtaining the seismic interval velocity of the source rock interval, and using sonic logging data and mud logging data to determine the pure mudstone velocity and pure sandstone velocity of the source rock interval, and substituting the velocities into formula (I) to calculate the mudstone percentage, wherein formula (I) is: V int represents the seismic layer velocity, V m represents the velocity of pure mudstone, V s represents the pure sandstone velocity, P m represents the percentage of mudstone; the fifth step is to obtain the thickness model of the source rock layer, and construct the mudstone thickness model of the source rock layer according to the mudstone percentage; the sixth step is to divide the mudstone thickness model of the source rock layer 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, and calculate the oil resources of each unit using formula (II), and the basin oil resources are obtained by accumulation, wherein formula (II): Q1 represents the amount of oil resources, n represents the total number of units, i ranges from 1 to n represents the unit number, A si represents the effective source rock area of unit i, H si represents the thickness of mudstone in unit i, ρ represents the density of source rock, C represents the organic carbon content, K c represents the organic matter conversion coefficient, K a represents the clustering coefficient.
[0040] In the present invention, the method for obtaining the effective source rock area and the mudstone thickness of the source rock layer is improved, thereby enhancing the accuracy of both.
[0041] In the present invention, before predicting the amount of oil resources, in a basin with sparse wells and low exploration, basin strata, structural information, two-dimensional or three-dimensional seismic data and seismic velocity spectrum are obtained through seismic data, and logging, well logging and source rock geochemical data of wells drilled in the area are collected.
[0042] Drilling data can be used to determine key parameters related to the hydrocarbon generation threshold. Source rock information includes at least pyrolysis parameters, vitrinite reflectance, and organic matter composition. This information can be used to determine the source rock's hydrocarbon generation potential and thermal evolution stage, thereby determining the hydrocarbon generation threshold depth.
[0043] Specifically, the hydrocarbon generation threshold depth mainly depends on indicators such as vitrinite reflectance, pyrolysis parameters, and geochemical profiles. By sparse drilling to obtain source rock samples, and through the integration of multiple parameters such as geochemistry, well logging, and pressure testing, the hydrocarbon generation threshold depth and hydrocarbon generation period can be accurately characterized. Vitrinite reflectance is obtained by vitrinite reflectance testing of drilling cores or cuttings. The total organic carbon content can be obtained through rock pyrolysis experiments or well logging curves, and rock pyrolysis parameters such as soluble hydrocarbon content and pyrolytic hydrocarbon content can also be obtained. The organic matter conversion efficiency can be obtained through rock sample solvent extraction experiments. Well logging curve parameters include acoustic wave time difference and resistivity, as well as density and porosity curves. Based on the above source rock information, ordinary technicians in this field can comprehensively determine the hydrocarbon generation threshold depth of the source rock layer. The determination process belongs to the existing technology and will not be described in detail in this invention.
[0044] The stratigraphic age corresponding to each stratum is obtained through drilling. After the hydrocarbon generation threshold depth is determined, the stratigraphic age corresponding to the hydrocarbon generation threshold depth in the drilling is determined, which is the hydrocarbon generation period.
[0045] On the basis of existing seismic data, paleo-tectonic restoration methods such as paleo-thickness restoration of strata, restoration of erosion thickness and restoration of paleo-fault throw are used to restore the paleo-structures during the hydrocarbon generation period. This includes using the amplitude reflection characteristics of seismic profiles and logging data to determine the stratigraphic interfaces during the key hydrocarbon generation period, and using layer-flattening technology to flatten the top boundary strata of the paleo-landform to the hydrocarbon generation period, thereby restoring the structural morphology of the bottom boundary of the paleo-landform formed during the erosion period.
[0046] The obtained paleostructures include three dimensions: the morphological plane distribution of paleostructures and the depth calibrated by contour lines.
[0047] On a morphological distribution map of paleostructures during the hydrocarbon generation period, contour lines are drawn at the hydrocarbon generation threshold depth, and the area below the threshold depth is defined as the mature source rock area. Within the mature source rock area, other types of blocks, such as sandstone, may also exist. These blocks lack hydrocarbon generation structures and need to be excluded to accurately determine the effective source rock area.
[0048] Dark mudstone refers to fine-grained sedimentary rocks rich in organic matter. Its color is black or grayish-black, reflecting a strongly reducing sedimentary environment. Its organic carbon content is typically above 0.5%, making it the primary source rock type in continental basins. The thickness and distribution of dark mudstone directly influence its hydrocarbon generation potential. The effective source rock area, defined as the continuous distribution of dark mudstone within the mature source rock range that meets the requirements for organic matter abundance, type, and hydrocarbon expulsion, serves as the basis for resource calculation.
[0049] In the present invention, delineating mature source rock areas based on paleostructures during the hydrocarbon generation period offers advantages over delineating mature source rock areas based on present-day structures: paleostructures during the hydrocarbon generation period can restore the burial depth and temperature conditions of the source rock during the critical hydrocarbon generation stage, thus avoiding misjudgment of maturity due to later tectonic uplift or denudation. This prevents misidentification of later structural highs as immature areas. It also clarifies the matching of oil and gas migration and accumulation pathways with reservoir formation, reducing interference from later tectonic alterations. This invention significantly improves the accuracy of mature source rock areas, providing accurate area data for resource calculations.
[0050] 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 further provide accurate area data for resource calculation.
[0051] The density and organic carbon content of the source rock are obtained through well logging, and the organic matter conversion coefficient and aggregation coefficient are obtained through regional statistics. These parameters can be obtained according to existing methods, and the present invention is not limited thereto. For example, the density of the source rock is directly measured by drilling the source rock core using a helium porosimeter or a saturated water weighing method. The organic matter conversion coefficient is obtained by simulating the hydrocarbon generation process of the source rock through a gold tube pyrolysis experiment, measuring the hydrocarbon production rate at different maturity levels, and establishing a relationship curve. The organic carbon content is directly measured by TOC and soluble organic matter content in the obtained core samples. The aggregation coefficient is obtained by using geological statistics to refer to the reservoir formation efficiency of similar basins.
[0052] After obtaining the above parameters, substitute them into the resource calculation formula (II) to obtain the resource volume of each unit, and then sum them up to obtain the oil resource volume of the basin. Q1 represents the amount of oil resources, n represents the total number of units, i ranges from 1 to n represents the unit number, A si represents the effective source rock area of unit i, H si represents the thickness of mudstone in unit i, ρ represents the density of source rock, C represents the organic carbon content, K c represents the organic matter conversion coefficient, K a represents the clustering coefficient.
[0053] The present invention also provides another method for predicting the amount of oil resources in a sparsely welled and poorly explored basin, comprising: a first step of obtaining seismic interval velocities of a source rock interval, using sonic logging data and mud logging data to determine the pure mudstone velocity and pure sandstone velocity of the source rock interval, and substituting the velocities into formula (I) to calculate the mudstone percentage, wherein formula (I) is: V int represents the seismic layer velocity, V m represents the velocity of pure mudstone, V s represents the pure sandstone velocity, P mrepresents the percentage of mudstone; the second step is to obtain the thickness model of the source rock layer, and construct the mudstone thickness model of the source rock layer according to the percentage of mudstone; the third step is to obtain the density of the source rock, the area of the source rock, the organic matter conversion coefficient, the organic carbon content and the aggregation coefficient, 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 represents the area of source rock, H s1 represents the thickness of mudstone, dA represents the differential unit of effective source rock area, dH represents the differential unit of mudstone thickness, ρ represents the density of source rock, C represents the organic carbon content, K c represents the organic matter conversion coefficient, K a represents the clustering coefficient.
[0054] That is, this scheme only improves the accuracy of mudstone thickness in the thickness dimension, and does not involve improvements in the area of source rocks and the improvement of unit division to improve calculation efficiency.
[0055] The present invention also provides another method for predicting the amount of oil resources in a basin with sparse wells and low exploration, comprising: a first step of determining the hydrocarbon generation threshold depth and hydrocarbon generation period of the source rock interval based on the source rock information obtained by drilling; a second step of restoring the paleostructure of the source rock interval 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 paleostructure; a third step of determining the distribution range of dark mudstone within the mature source rock area in the source rock interval to obtain the effective source rock area; a fourth step of obtaining the density of the source rock, the mudstone thickness of the source rock interval, the organic matter conversion coefficient, the organic carbon content, and the aggregation coefficient, and calculating the amount of oil resources in the basin using formula (II).
[0056] Wherein, formula (II): Q2 represents the amount of oil resources in the basin, A s2 represents the effective source rock area, H s2 represents the thickness of mudstone, dA represents the differential unit of effective source rock area, dH represents the differential unit of mudstone thickness, ρ represents the density of source rock, C represents the organic carbon content, K c represents the organic matter conversion coefficient, K a represents the clustering coefficient.
[0057] That is, this scheme only provides a more accurate effective source rock area, and does not involve improvements in the thickness dimension to improve the accuracy of mudstone thickness and the unit division to improve the calculation efficiency.
[0058] In this paper, by delineating the area of mature source rock based on paleostructures during the hydrocarbon generation period and eliminating interference from non-source rock, accurate area data is provided for resource calculation. By eliminating interference from non-source rock in thickness, accurate thickness data is provided for resource calculation. Calculating resource volume by dividing the unit into units simplifies computational complexity and improves efficiency and accuracy.
[0059] Although a number of embodiments of the present 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. Those skilled in the art may devise numerous modifications, variations, and alternatives without departing from the concept and spirit of the present invention. It should be understood that in practicing the present invention, various alternatives to the embodiments of the present invention described herein may be employed. The appended claims are intended to define the scope of the present invention and therefore cover equivalents or alternatives within the scope of these claims.
Claims
1. A method for predicting the thickness of dark mudstone in a sparsely welled and poorly explored basin, characterized in that: include: The first step is to obtain the seismic layer velocity of the source rock layer and the thickness model of the source rock layer; The second step is to use the sonic logging data and the mud logging data to obtain the pure mudstone velocity and the pure sandstone velocity of the source rock interval; The third step is to substitute the seismic interval velocity of the source rock layer, the pure mudstone velocity and the pure sandstone velocity of the source rock layer into the calculation formula (I) to calculate the mudstone percentage. Wherein, formula (I): V int represents the seismic layer velocity, V m represents the velocity of pure mudstone, V s represents the pure sandstone velocity, P m Indicates the percentage of mudstone; The fourth step is to obtain a plane distribution map of seismic facies types of the source rock interval, and use the organic facies characteristics and types of the source rock obtained by drilling to mark the dark mudstone in the plane distribution map of seismic facies types and determine the distribution range of the dark mudstone; The fifth step is to construct a dark mudstone thickness model within the distribution range of the dark mudstone according to the thickness model and the mudstone percentage.
2. The method according to claim 1, characterized in that In the first step, the seismic two-way time of the top and bottom interfaces of the source rock layer is picked up, and the thickness of the source rock layer is calculated using the time-depth relationship data to obtain the source rock layer thickness model.
3. The method according to claim 1, characterized in that In the second step, the pure mudstone velocity and pure sandstone velocity of the source rock interval are obtained by the following method: Using sonic logging and mud logging data to interpret the sonic transit time of pure mudstone and pure sandstone; Fitting the relationship between pure mudstone velocity and burial depth; Fitting the relationship between pure sandstone velocity and burial depth; Obtain the burial depth of the source rock layer and calculate the pure mudstone velocity and pure sandstone velocity of the source rock layer.
4. The method according to claim 3, characterized in that The burial depth of the source rock layer is taken as the depth of the top interface, the bottom interface or the midpoint of the top interface and the bottom interface.
5. The method according to claim 3, characterized in that The relationship between the velocity of pure mudstone and the burial depth is obtained by using any of the following methods: polynomial fitting, least square fitting and piecewise linear fitting.
6. The method according to claim 1, characterized in that The source rock layer thickness in the source rock layer thickness model is multiplied by the mudstone percentage to obtain the mudstone thickness model of the source rock layer.
7. A method for predicting the thickness of dark mudstone in a sparsely welled and poorly explored basin, characterized in that: include: The first step is to obtain the seismic layer velocity of the source rock layer and the thickness model of the source rock layer; The second step is to use the sonic logging data and the mud logging data to fit the relationship between the velocity and the burial depth of pure mudstone, and to use the sonic logging data and the mud logging data to fit the relationship between the velocity and the burial depth of pure sandstone; The third step is to divide the thickness model of the source rock layer into a plurality of units vertically, and obtain the seismic interval velocity, pure mudstone velocity and pure sandstone velocity of the source rock layer in each unit; The fourth step is to substitute the seismic interval velocity, pure mudstone velocity and pure sandstone velocity of each unit's source rock layer into the calculation formula (I) to calculate the mudstone percentage of each unit. Wherein, formula (I): V int represents the seismic layer velocity, V m represents the velocity of pure mudstone, V s represents the pure sandstone velocity, P m Indicates the percentage of mudstone; The fifth step is to obtain a plane distribution map of seismic facies types of the source rock interval, and use the organic facies characteristics and types of the source rock obtained by drilling to mark the dark mudstone in the plane distribution map of seismic facies types and determine the distribution range of the dark mudstone; The sixth step is to construct a dark mudstone thickness model of the source rock layer according to the thickness model of the source rock layer and the mudstone percentage of each unit within the distribution range of the dark mudstone.
8. The method according to claim 7, characterized in that The cells are divided into integer multiples of the seismic line grid size.
9. The method according to claim 7, characterized in that The cells are divided according to depth contours.
10. The method according to claim 7, characterized in that In the sixth step, the average thickness of each unit of the thickness model of the source rock layer segment is multiplied by the mudstone percentage to obtain the mudstone thickness model of the source rock layer segment.