A method and device for evaluating a mineral reservoir and an electronic device

Abstract

This invention provides a method, apparatus, electronic device, and storage medium for evaluating mineral reservoirs. The method includes: acquiring well logging data of a glauconite sandstone to be tested; determining a well logging interpretation model for the glauconite sandstone based on the well logging data; optimizing the well logging interpretation model to obtain an objective function; determining glauconite sandstone reservoir parameters based on the minimum value of the objective function; determining the oil saturation of the glauconite sandstone based on the reservoir parameters; and determining the oil-bearing standard of the glauconite sandstone based on the oil saturation. This method achieves effective reservoir identification.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, and specifically to a method, apparatus, electronic device and storage medium for evaluating mineral reservoirs. Background Technology

[0002] Glauconite sandstone is a widely distributed rock type globally, primarily developed in marine sedimentary environments. Under certain conditions, subsurface glauconite sandstone can serve as a favorable reservoir medium for oil and gas. The mineral composition of glauconite sandstone formations is complex, exhibiting similar response characteristics on well logging curves to mudstone, silty mudstone, and argillaceous siltstone formations, namely medium to high gamma rays, high density, and low resistivity. Existing traditional well logging interpretation models are relatively fixed, making it difficult to fully extract effective information from well logging data in complex formations like glauconite sandstone. The calculation process mimics classic "manual" interpretation steps, leading to significant deviations in the calculated results of reservoir parameters such as clay content, porosity, and saturation. Summary of the Invention

[0003] The purpose of this invention is to provide a method, apparatus, electronic device, and storage medium for evaluating mineral reservoirs, which enables effective reservoir identification.

[0004] To achieve the above objectives, embodiments of the present invention provide a method for evaluating mineral reservoirs, the method comprising:

[0005] Obtain well logging data for the glauconite sandstone to be tested;

[0006] The well logging interpretation model for the glauconite sandstone to be tested is determined based on the well logging data.

[0007] The objective function is obtained by optimizing the well logging interpretation model, and the parameters of the glauconite sandstone reservoir are determined based on the minimum value of the objective function.

[0008] The oil saturation of the glauconite sandstone was determined based on the reservoir parameters.

[0009] The oil layer standard of the glauconite sandstone to be tested is determined based on the oil saturation.

[0010] Optionally, determining the well logging interpretation model for the glauconite sandstone to be tested based on the well logging data includes:

[0011] The initial glauconite content, initial quartz content, and initial clay content were determined based on the well logging data.

[0012] The well logging interpretation model for the glauconite sandstone to be tested is determined based on the initial clay content, initial glauconite content, initial quartz content, glauconite framework parameters, and quartz framework parameters.

[0013] The logging data includes: core glauconite content, density and neutron porosity difference, core quartz content, gamma-ray sonic neutron density, density and neutron frequency cross-plot data, glauconite framework parameters, and quartz framework parameters.

[0014] Optionally, determining the initial glauconite content, initial quartz content, and initial clay content based on the well logging data includes:

[0015] The initial glaucon content was determined based on the glaucon content in the core and the density and neutron porosity difference.

[0016] The initial quartz content is determined based on the core quartz content and the gamma-ray sonic neutron density; and...

[0017] The clay skeleton parameters are determined based on the density and neutron frequency cross-section data, and the initial clay content is obtained by calculating the clay skeleton parameters using gamma.

[0018] Optionally, the objective function includes:

[0019]

[0020]

[0021] Wherein, F(GL) is the objective function obtained by optimizing the well logging interpretation model;

[0022] n is the number of well logging series response equations;

[0023] m i The value is the actual measured value of the logging curve, and i is the i-th logging curve;

[0024] The square of the measurement error of the actual logging curve;

[0025] The error in the logging response equation;

[0026] p is the number of constraints;

[0027] g j This is the j-th type of constraint;

[0028] ε is the constraint error of the j-th constraint condition;

[0029] f i The theoretical response value of the well logging interpretation model.

[0030] m represents the number of components in the glauconite sandstone to be tested that require a solution.

[0031] Let be the relative volume vector of the components in the glauconite sandstone to be tested, which is the solution required.

[0032] e ij This is a regional interpretation parameter vector.

[0033] Optionally, the relative volume vector of the components in the glauconite sandstone to be tested needs to be solved. for:

[0034]

[0035] Among them, V cl V represents the initial clay content. gl For glauconite framework parameters, V qz These are the quartz stone skeleton parameters.

[0036] Optionally, the well logging response equation is:

[0037]

[0038] in, Let be the relative volume vector of the components in the glauconite sandstone to be tested, which is the solution required.

[0039] GR is the natural gamma of the well log, DT is the sonic transit time, NPHI is the neutron, and RHOB is the density.

[0040] Optionally, determining the oil saturation of the glauconite sandstone based on the reservoir parameters includes:

[0041] S o =1-S w =1-10 [a+blog(RESD)+clog(PHIT)]

[0042] Among them, S o Oil saturation;

[0043] S w Water saturation;

[0044] RESD is resistivity logging data.

[0045] PHIT stands for porosity.

[0046] a, b, and c are the relationship factors between the original oil saturation data obtained from closed core samples of glauconite sandstone and the resistivity logging data and porosity.

[0047] Optionally, the glauconite sandstone reservoir parameters include: actual clay content, actual quartz content, actual glauconite content, and porosity.

[0048] On the other hand, the present invention provides an evaluation device for mineral reservoirs, the device comprising:

[0049] The acquisition module is used to acquire well logging data of the glauconite sandstone to be tested;

[0050] The first processing module is used to determine the well logging interpretation model of the glauconite sandstone to be tested based on the well logging data;

[0051] The second processing module is used to optimize the well logging interpretation model to obtain the objective function, and determine the glauconite sandstone reservoir parameters based on the minimum value of the objective function.

[0052] The third processing module is used to determine the oil saturation of the glauconite sandstone based on the glauconite sandstone reservoir parameters.

[0053] The fourth processing module is used to determine the oil layer standard of the glauconite sandstone to be tested based on the oil saturation.

[0054] Optionally, determining the well logging interpretation model for the glauconite sandstone to be tested based on the well logging data includes:

[0055] The initial glauconite content, initial quartz content, and initial clay content were determined based on the well logging data.

[0056] The well logging interpretation model for the glauconite sandstone to be tested is determined based on the initial clay content, initial glauconite content, initial quartz content, glauconite framework parameters, and quartz framework parameters.

[0057] The logging data includes: core glauconite content, density and neutron porosity difference, core quartz content, gamma-ray sonic neutron density, density and neutron frequency cross-plot data, glauconite framework parameters, and quartz framework parameters.

[0058] Optionally, determining the initial glauconite content, initial quartz content, and initial clay content based on the well logging data includes:

[0059] The initial glaucon content was determined based on the glaucon content in the core and the density and neutron porosity difference.

[0060] The initial quartz content is determined based on the core quartz content and the gamma-ray sonic neutron density; and...

[0061] The clay skeleton parameters are determined based on the density and neutron frequency cross-section data, and the initial clay content is obtained by calculating the clay skeleton parameters using gamma.

[0062] Optionally, the objective function includes:

[0063]

[0064]

[0065] Wherein, F(GL) is the objective function obtained by optimizing the well logging interpretation model;

[0066] n is the number of well logging series response equations;

[0067] m i The value is the actual measured value of the logging curve, and i is the i-th logging curve;

[0068] The square of the measurement error of the actual logging curve;

[0069] The error in the logging response equation;

[0070] p is the number of constraints;

[0071] g j This is the j-th type of constraint;

[0072] ε is the constraint error of the j-th constraint condition;

[0073] f i The theoretical response value of the well logging interpretation model.

[0074] m represents the number of components in the glauconite sandstone to be tested that require a solution.

[0075] Let be the relative volume vector of the components in the glauconite sandstone to be tested, which is the solution required.

[0076] e ij This is a regional interpretation parameter vector.

[0077] Optionally, the relative volume vector of the components in the glauconite sandstone to be tested needs to be solved. for:

[0078]

[0079] Among them, V cl V represents the initial clay content. gl For glauconite framework parameters, V qz These are the quartz stone skeleton parameters.

[0080] On the other hand, the present invention also provides the electronic device comprising:

[0081] At least one processor;

[0082] A memory connected to the at least one processor;

[0083] The memory stores instructions that can be executed by the at least one processor, which implements the mineral reservoir evaluation method described above by executing the instructions stored in the memory.

[0084] On the other hand, the present invention also provides a machine-readable storage medium storing machine instructions that, when the machine instructions are executed on a machine, cause the machine to perform the above-described method for evaluating mineral reservoirs.

[0085] The mineral reservoir evaluation method of the present invention includes: acquiring well logging data of a glauconite sandstone to be tested; determining a well logging interpretation model for the glauconite sandstone based on the well logging data; optimizing the well logging interpretation model to obtain an objective function; determining the glauconite sandstone reservoir parameters based on the minimum value of the objective function; determining the oil saturation of the glauconite sandstone based on the reservoir parameters; and determining the oil layer standard of the glauconite sandstone based on the oil saturation. This method utilizes conventional well logging data to optimize reservoir parameter evaluation, achieving effective reservoir identification and providing scientific and technical support for decision-making in exploration wells, evaluation deployment, old well review, and new project site selection.

[0086] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0087] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0088] Figure 1 This is a flowchart illustrating a method for evaluating mineral reservoirs according to the present invention.

[0089] Figure 2 This is a flowchart illustrating a specific embodiment of the present invention;

[0090] Figure 3 This is a frequency diagram of neutron-density intersection in the target layer;

[0091] Figure 4 This is a comparison chart of neutron density cross-plots and natural gamma calculations of clay content curves;

[0092] Figure 5 This is a graph showing the relationship between density neutron porosity difference and glauconite sandstone and sandstone.

[0093] Figure 6 This is a graph showing the fitting relationship between glauconite content and density neutron porosity difference in core analysis;

[0094] Figure 7 It shows the correlation between quartz content in core analysis and various single curves and the multivariate fitting relationship of multiple curves;

[0095] Figure 8This is a comparison chart of core analysis and optimized well logging interpretation results for the target layer's glauconite sandstone section. Detailed Implementation

[0096] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0097] The rock matrix framework of glauconite sandstone reservoirs is composed of quartz and glauconite, with clay minerals primarily filling the pores. Current multi-mineral logging evaluation methods mainly employ optimal logging interpretation. This approach breaks away from traditional logging interpretation models and logical concepts. Instead of directly calculating reservoir parameters using limited logging data and response equations, it utilizes generalized inversion theory from geophysics. Based on actual logging values ​​that have been corrected for environmental impacts and more accurately reflect formation characteristics, and using appropriate interpretation models and various logging response equations, it back-calculates corresponding theoretical logging values ​​using rationally selected regional interpretation parameters and initial reservoir parameter values. These theoretical values ​​are then compared with actual logging values, and a suitable objective function is established based on the principle of nonlinear weighted least squares and error theory. By continuously adjusting the values ​​of various unknown reservoir parameters, the error of the objective function is minimized, thereby obtaining the optimal logging interpretation result.

[0098] The inventors discovered through research that, because the logging response characteristics of glauconite are extremely similar to those of clay minerals—exhibiting high gamma, high density, high neutron content, and low resistivity—in optimized logging interpretation calculations, glauconite, which serves as a matrix framework component in glauconite sandstone, is often mistakenly counted as a clay mineral filling interstitial spaces, leading to deviations in reservoir evaluation results. To address this issue, this invention proposes a method for evaluating mineral reservoirs.

[0099] Figure 1 This is a flowchart illustrating a method for evaluating mineral reservoirs according to the present invention, as shown below. Figure 1 As shown, the evaluation method for the mineral reservoir includes the following steps: Step S101 is to obtain well logging data of the glauconite sandstone to be tested. The well logging data includes: glauconite content in the core, density and neutron porosity difference, quartz content in the core, gamma-ray sonic neutron density, density and neutron frequency cross-plot data, glauconite framework parameters, and quartz framework parameters.

[0100] Step S102 involves determining the well logging interpretation model for the glauconite sandstone to be tested based on the well logging data. Specifically, determining the well logging interpretation model for the glauconite sandstone to be tested based on the well logging data includes: determining the initial glauconite content, initial quartz content, and initial clay content based on the well logging data; and determining the well logging interpretation model for the glauconite sandstone to be tested based on the initial clay content, initial glauconite content, initial quartz content, glauconite framework parameters, and quartz framework parameters.

[0101] The step of determining the initial glauconite content, initial quartz content, and initial clay content based on the well logging data includes: determining the initial glauconite content based on the core glauconite content and the density-neutron porosity difference; determining the initial quartz content based on the core quartz content and the gamma-ray sonic neutron density; and determining clay skeleton parameters based on the density-neutron frequency intersection data, and obtaining the initial clay content by calculating the clay skeleton parameters using gamma.

[0102] According to a specific implementation method, such as Figure 2 As shown, the dry clay skeleton parameters are determined using a frequency cross-plot of neutron density logging data. These parameters are used to calculate the clay content and as input parameters for the optimized logging interpretation model. Logging data within the target layer are selected, and a frequency cross-plot of neutron and density logging data is established to determine the neutron value and skeleton density value of the dry clay skeleton.

[0103] The dry clay skeleton parameters are used as input parameters for calculating clay content using the neutron density cross-plot method. The clay content calculated using this method is then compared with that calculated using natural gamma ray spectroscopy to optimize the picking of baseline parameters for natural gamma sand and mudstone. These dry clay skeleton parameters are also used as input parameters for the optimized well logging interpretation model.

[0104] Then, the initial value of clay content was calculated using natural gamma-ray logging data. The clay content was calculated by picking the natural gamma-ray sandstone baseline and mudstone baseline, and compared with the clay content calculated by the neutron density intersection method to further optimize the parameters of the natural gamma-ray sandstone and mudstone baselines and obtain a more reliable clay content. The dry clay skeleton parameters were used as the constraint conditions for the clay content in the optimized logging interpretation model.

[0105] The relationship between glauconite content from core analysis and glauconite-sensitive well logging data was established, and the initial value of glauconite mineral content was calculated. Based on the response relationship analysis between different series of well logging data in glauconite sandstone formations, the difference between indicator density porosity and neutron porosity data was found to be the most sensitive to the identification of glauconite sandstone and quartz sandstone, showing a good positive correlation with glauconite content. Using the glauconite mineral content analyzed from core experiments, a fitting relationship was established between glauconite content and the difference between density porosity and neutron porosity data, and the preprocessed value of glauconite content was calculated using this relationship.

[0106] The preprocessed glauconite content was used as the constraint input for the optimal well logging interpretation model. The relationship between core analysis quartz content and well logging data was established. The initial quartz mineral content was calculated. By comparing the pairwise correlations between quartz content and each well logging curve series, the single-curve correlations for natural gamma ray, sonic transit time, neutrons, and density were generally low, but the multivariate correlations were high. Using the quartz mineral content from core experiments, a multivariate linear fitting relationship was established between quartz content and natural gamma ray, sonic transit time, neutrons, and density. The preprocessed quartz content value was then calculated using this relationship.

[0107] Step S103 involves optimizing the well logging interpretation model to obtain the objective function, and determining the glauconite sandstone reservoir parameters based on the minimum value of the objective function. Specifically, the glauconite sandstone reservoir parameters include: actual clay content, actual quartz content, actual glauconite content, and porosity. The objective function obtained by optimizing the well logging interpretation model is achieved by using the preprocessed quartz content value as the constraint condition for optimizing the quartz content in the well logging interpretation model.

[0108] Specifically, the objective function includes:

[0109]

[0110]

[0111] Where F(GL) is the objective function obtained by optimizing the well logging interpretation model; n is the number of well logging series response equations; m i The value is the actual measured value of the logging curve, and i is the i-th logging curve; The square of the measurement error of the actual logging curve; The error in the logging response equation is represented by p; the number of constraints is represented by g. j Let f be the j-th constraint condition; ε be the constraint error of the j-th constraint condition; i The value represents the theoretical response of the well logging interpretation model, where m is the number of components in the glauconite sandstone to be tested that require a solution. Let e ​​be the relative volume vector of the components in the glauconite sandstone to be tested, which is the solution required for the composition. ijThis is a regional interpretation parameter vector.

[0112] The relative volume vectors of the components in the glauconite sandstone to be tested need to be solved. for:

[0113]

[0114] Among them, V cl V represents the initial clay content. gl For glauconite framework parameters, V qz These are the quartz stone skeleton parameters.

[0115] The well logging response equation is:

[0116]

[0117] in, Let GR be the relative volume vector of the components to be solved in the glauconite sandstone to be tested, DT be the natural gamma of the well log, NPHI be the sonic transit time, and RHOB be the density.

[0118] In one specific implementation method, the initial clay content from natural gamma pretreatment, the initial values ​​of the glauconite and quartz skeleton mineral content (i.e., glauconite skeleton parameters and quartz skeleton parameters) converted from core experimental information, and the sonic transit time, density, and neutron continuous dataset from well logging are used as inputs to the optimized well logging interpretation model. An optimized well logging interpretation objective function suitable for glauconite sandstone reservoir evaluation is established, and the objective function suitable for optimized well logging interpretation of glauconite sandstone is solved based on the principle of nonlinear weighted least squares and error theory.

[0119] Specifically, this includes: the initial value of clay content from natural gamma pretreatment, and the initial value of the dual mineral content V of glauconite and quartz in the rock framework based on core experimental information. cl V gl V qz Using continuous datasets of natural gamma ray (GR), sonic transit time (DT), neutron (NPHI), and density (RHOB) from well logging as inputs to the optimized well logging interpretation model, and based on the well logging interpretation model established for the rock matrix framework composed of quartz and glauconite minerals in glauconite sandstone formations, a set of well logging response equations can be obtained.

[0120] The minimum value of the objective function is found by using optimization methods to obtain various parameters of the glauconite sandstone reservoir, including clay content, quartz content, glauconite content, and porosity. The glauconite sandstone reservoir is determined based on the threshold values ​​of the reservoir parameters.

[0121] Step S104 involves determining the oil saturation of the glauconite sandstone based on the reservoir parameters. Specifically, determining the oil saturation of the glauconite sandstone based on the reservoir parameters includes:

[0122] S o =1-S w =1-10 [a+blog(RESD)+clog(PHIT)]

[0123] Among them, S o Oil saturation; S w 1 represents water saturation; RSD represents resistivity logging data; PHIT represents porosity; and a, b, and c are the relationship factors between the original oil saturation data obtained from closed core samples of glauconite sandstone and the resistivity logging data and porosity.

[0124] Step S105 involves determining the oil-bearing reservoir standard of the glauconite sandstone to be tested based on the oil saturation. Specifically, the oil-bearing reservoir standard of the glauconite sandstone is determined by combining the fluid response characteristics of the well test and gas logging.

[0125] This invention utilizes microstructural analysis of rock samples, based on conventional logging data and with core experimental data as prior constraints, to optimize reservoir parameter evaluation methods for glauconite sandstone reservoirs. It reveals that the matrix framework of glauconite sandstone reservoirs is composed of both quartz and glauconite, with clay minerals primarily filling the pores. Therefore, a multi-mineral logging evaluation technique based on an optimized logging interpretation model is employed, breaking through traditional logging interpretation methods. Based on generalized inversion theory in geophysics, and using appropriate interpretation models and various logging response equations, the corresponding theoretical logging values ​​are calculated by rationally selecting regional interpretation parameters and initial reservoir parameter values. An objective function is established using the nonlinear weighted least squares method, thereby obtaining various reservoir parameters of glauconite sandstone. This provides scientific and technical support for decision-making in exploration wells, evaluation deployment, old well review, and new project site selection.

[0126] The present invention also proposes another specific implementation method. Taking the Cretaceous glauconite sandstone strata as an example, the specific method includes:

[0127] (1) Select neutron and density logging data that are more sensitive to reservoir parameters within the target interval from conventional logging data, and establish a neutron and density data cross-plot (e.g., Figure 3 As shown in the figure, the neutron value and skeleton density value of the dry clay skeleton are determined, and the clay content is calculated by the neutron-density cross-section method.

[0128] (2) Based on the clay content calculated in (1), the clay content calculated using natural gamma is further compared, and the picking of baseline parameters for natural gamma sand and mudstone is optimized. Finally, the clay content calculated using natural gamma is obtained as a constraint condition for the optimal well logging interpretation model. Figure 3 As shown, the neutron and density frequency cross-plot of the target layer encountered in multiple wells (numbers, letters, and * represent the frequency of data points; 1-9 times are represented by numbers, 9-35 times by letters A, Z, and *, and more than 35 times by *) indicates that triangle ① identifies the wet clay point, triangle ② identifies the dry clay skeleton point, the neutron concentration is 0.49 (v / v), and the density is 2.68 (g / cc). The comparison results of clay content calculated by the neutron density cross-plot method and the clay content calculated by the natural gamma method are shown below. Figure 4 The two are similar.

[0129] (3) Establish the relationship between glauconite content in core analysis and well logging datasets sensitive to glauconite, and calculate the initial value of glauconite minerals. Through the response relationship between various well logging curves of glauconite sandstone and quartz sandstone, the density-porosity and neutron porosity differences are most sensitive to the identification of glauconite sandstone and quartz sandstone. The density-neutron porosity difference is greater than 0.05 for glauconite sandstone and less than 0.05 for sandstone. When the density-neutron porosity difference is less than 0.05, the glauconite content rapidly approaches 0 (e.g., ...). Figure 5 (As shown). Therefore, a non-linear relationship was established between the glauconite content and the density neutron porosity difference in core analysis, with the fitting formula being y = 0.664 + 0.205 * ln(x), and the correlation coefficient r = 0.9044 (as shown). Figure 6 (As shown). The preprocessed value of glauconite content was calculated based on this relationship and used as a constraint condition for the glauconite content in the optimal well logging interpretation model.

[0130] (4) Establish the relationship between quartz content in core analysis and logging curves, and calculate the initial values ​​of quartz minerals. Through pairwise correlation comparisons between quartz content and various logging datasets, the overall single-curve correlations of natural gamma, sonic transit time, neutrons, and density are low, but the multivariate correlation is high. Therefore, using the quartz mineral content analyzed in core experiments, a multivariate linear fitting relationship between quartz content and natural gamma, sonic transit time, neutrons, and density was established, with a correlation as high as 95% (e.g., ...). Figure 7 (As shown). The preprocessed quartz content value is calculated based on this relationship and used as a constraint condition for the quartz content in the optimal logging interpretation model.

[0131] (5) Optimal well logging reservoir parameter evaluation was conducted under various constraints based on core experimental information conversion. The initial values ​​of clay content preprocessed with natural gamma ray, the initial values ​​of glauconite and quartz mineral content converted from core experimental information, and well logging sonic transit time, density, and neutron continuity data were used as inputs to the optimal well logging interpretation model to obtain various reservoir parameter values ​​for glauconite sandstone, including clay content, quartz content, glauconite content, and porosity. These calculated parameter values ​​showed high agreement with the measured mineral contents from the core samples (e.g., ...). Figure 8 (As shown). Glauconite sandstone reservoirs were determined based on reservoir porosity parameter thresholds.

[0132] (6) Finally, based on the glauconite sandstone reservoir determined in (5), the saturation model formula for the glauconite sandstone reservoir is further used to calculate: S o =1-S w =1-10 [a+blog(RESD)+clog(PHIT)] Using the relationship between the oil saturation and resistivity (RESD) logging data of the target formation and the porosity data calculated in (5) above, the calculation parameters a = 1.125, b = 0.124, and c = 0.798 in the formula were determined. Thus, the oil saturation of the glauconite sandstone was obtained, and the oil layer of the glauconite sandstone was determined based on the oil saturation threshold value, such as... Figure 8 As shown.

[0133] The present invention also provides a mineral reservoir evaluation device, comprising: an acquisition module for acquiring well logging data of a glauconite sandstone to be tested; a first processing module for determining a well logging interpretation model of the glauconite sandstone to be tested based on the well logging data; a second processing module for optimizing the well logging interpretation model to obtain an objective function, and determining glauconite sandstone reservoir parameters based on the minimum value of the objective function; a third processing module for determining the oil saturation of the glauconite sandstone based on the glauconite sandstone reservoir parameters; and a fourth processing module for determining the oil layer standard of the glauconite sandstone to be tested based on the oil saturation.

[0134] The step of determining the well logging interpretation model of the glauconite sandstone to be tested based on the well logging data includes: determining the initial glauconite content, initial quartz content, and initial clay content based on the well logging data; determining the well logging interpretation model of the glauconite sandstone to be tested based on the initial clay content, initial glauconite content, initial quartz content, glauconite framework parameters, and quartz framework parameters; the well logging data includes: core glauconite content, density and neutron porosity difference, core quartz content, gamma-ray sonic neutron density, density and neutron frequency cross-plot data, glauconite framework parameters, and quartz framework parameters.

[0135] The step of determining the initial glauconite content, initial quartz content, and initial clay content based on the well logging data includes: determining the initial glauconite content based on the core glauconite content and the density-neutron porosity difference; determining the initial quartz content based on the core quartz content and the gamma-ray sonic neutron density; and determining clay skeleton parameters based on the density-neutron frequency intersection data, and obtaining the initial clay content by calculating the clay skeleton parameters using gamma.

[0136] The objective function includes:

[0137]

[0138]

[0139] Where F(GL) is the objective function obtained by optimizing the well logging interpretation model; n is the number of well logging series response equations; m i The value is the actual measured value of the logging curve, and i is the i-th logging curve; The square of the measurement error of the actual logging curve; The error in the logging response equation is represented by p; the number of constraints is represented by g. j Let f be the j-th constraint condition; ε be the constraint error of the j-th constraint condition; i The value represents the theoretical response of the well logging interpretation model, where m is the number of components in the glauconite sandstone to be tested that require a solution. Let e ​​be the relative volume vector of the components in the glauconite sandstone to be tested, which is the solution required for the composition. ij This is a regional interpretation parameter vector.

[0140] The relative volume vectors of the components in the glauconite sandstone to be tested need to be solved. for:

[0141]

[0142] Among them, V cl V represents the initial clay content. gl For glauconite framework parameters, V qz These are the quartz stone skeleton parameters.

[0143] This device transforms the scattered data of glauconite and quartz content from core experiments into a continuous data set of related mineral content preprocessed by establishing a functional relationship with the continuous data set of well logging. This data serves as a constraint condition in the optimal well logging interpretation model, thereby obtaining more accurate glauconite sandstone reservoir parameters to meet the needs of subsequent related research.

[0144] On the other hand, the present invention also provides an electronic device comprising: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor implements the above-described method for evaluating mineral reservoirs by executing the instructions stored in the memory.

[0145] The processor contains a kernel that retrieves the corresponding program units from memory. One or more kernels can be configured, and effective reservoir identification can be achieved by adjusting kernel parameters.

[0146] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0147] This invention provides a storage medium storing a program that, when executed by a processor, implements a method for evaluating mineral reservoirs.

[0148] This invention provides a processor for running a program, wherein the program executes an evaluation method for the mineral reservoir.

[0149] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: (method claim steps, exclusive claim + subordinate claim). The device described herein can be a server, PC, PAD, mobile phone, etc.

[0150] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having the following method steps: acquiring well logging data of a glauconite sandstone to be tested; determining a well logging interpretation model of the glauconite sandstone to be tested based on the well logging data; optimizing the well logging interpretation model to obtain an objective function; determining glauconite sandstone reservoir parameters based on the minimum value of the objective function; determining the oil saturation of the glauconite sandstone based on the glauconite sandstone reservoir parameters; and determining the oil layer standard of the glauconite sandstone to be tested based on the oil saturation. Optionally, determining the well logging interpretation model of the glauconite sandstone to be tested based on the well logging data includes: determining the initial glauconite content, initial quartz content, and initial clay content based on the well logging data; determining the well logging interpretation model of the glauconite sandstone to be tested based on the initial clay content, initial glauconite content, initial quartz content, glauconite framework parameters, and quartz framework parameters; the well logging data includes: core glauconite content, density and neutron porosity difference, core quartz content, gamma-ray sonic neutron density, density and neutron frequency cross-plot data, glauconite framework parameters, and quartz framework parameters. The determination of the initial glauconite content, initial quartz content, and initial clay content based on the well logging data includes: determining the initial glauconite content based on the core glauconite content and the density-neutron porosity difference; determining the initial quartz content based on the core quartz content and the gamma-ray sonic neutron density; and determining clay skeleton parameters based on the density-neutron frequency cross-plot data, and obtaining the initial clay content by calculating the clay skeleton parameters using gamma-ray. The glauconite sandstone reservoir parameters include: actual clay content, actual quartz content, actual glauconite content, and porosity.

[0151] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0152] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0153] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0154] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0155] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0156] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0157] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0158] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0159] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for evaluating mineral reservoirs, characterized in that, The method includes: Obtain well logging data for the glauconite sandstone to be tested; The well logging interpretation model for the glauconite sandstone to be tested is determined based on the well logging data. The objective function is obtained by optimizing the well logging interpretation model, and the parameters of the glauconite sandstone reservoir are determined based on the minimum value of the objective function. The oil saturation of the glauconite sandstone was determined based on the reservoir parameters. The oil layer standard of the glauconite sandstone to be tested is determined based on the oil saturation. The step of determining the well logging interpretation model for the glauconite sandstone to be tested based on the well logging data includes: The initial glauconite content, initial quartz content, and initial clay content were determined based on the well logging data. The well logging interpretation model for the glauconite sandstone to be tested is determined based on the initial clay content, initial glauconite content, initial quartz content, glauconite framework parameters, and quartz framework parameters. The logging data includes: core glauconite content, density and neutron porosity difference, core quartz content, gamma-ray sonic neutron density, density and neutron frequency cross-plot data, glauconite framework parameters, and quartz framework parameters. The determination of the initial glauconite content, initial quartz content, and initial clay content based on the well logging data includes: The initial glaucon content was determined based on the glaucon content in the core and the density and neutron porosity difference. The initial quartz content is determined based on the core quartz content and the gamma-ray sonic neutron density; and... The clay skeleton parameters are determined based on the density and neutron frequency cross-section data, and the initial clay content is obtained by calculating the clay skeleton parameters using gamma.

2. The method according to claim 1, characterized in that, The determination of the oil saturation of the glauconite sandstone based on the reservoir parameters includes: in, Oil saturation; Water saturation; RESD is resistivity logging data. PHIT stands for porosity. The relationship between the original oil saturation data, resistivity logging data, and porosity obtained from sealed core samples of glauconite sandstone.

3. The method according to claim 2, characterized in that, The glauconite sandstone reservoir parameters include: actual clay content, actual quartz content, actual glauconite content, and porosity.

4. A device for evaluating mineral reservoirs, characterized in that, The device includes: The acquisition module is used to acquire well logging data of the glauconite sandstone to be tested; The first processing module is used to determine the well logging interpretation model of the glauconite sandstone to be tested based on the well logging data; The step of determining the well logging interpretation model for the glauconite sandstone to be tested based on the well logging data includes: determining the initial glauconite content, initial quartz content, and initial clay content based on the well logging data; determining the well logging interpretation model for the glauconite sandstone to be tested based on the initial clay content, initial glauconite content, initial quartz content, glauconite framework parameters, and quartz framework parameters; the well logging data includes: core glauconite content, density and neutron porosity difference, core quartz content, gamma-ray sonic neutron density, density and neutron frequency cross-plot data, glauconite framework parameters, and quartz framework parameters; The step of determining the initial glauconite content, initial quartz content, and initial clay content based on the well logging data includes: determining the initial glauconite content based on the core glauconite content and the density-neutron porosity difference; determining the initial quartz content based on the core quartz content and the gamma-ray sonic neutron density; and determining clay skeleton parameters based on the density-neutron frequency intersection data, and obtaining the initial clay content by calculating the clay skeleton parameters using gamma. The second processing module is used to optimize the well logging interpretation model to obtain the objective function, and determine the glauconite sandstone reservoir parameters based on the minimum value of the objective function. The third processing module is used to determine the oil saturation of the glauconite sandstone based on the glauconite sandstone reservoir parameters. The fourth processing module is used to determine the oil layer standard of the glauconite sandstone to be tested based on the oil saturation.

5. The apparatus according to claim 4, characterized in that, The determination of the oil saturation of the glauconite sandstone based on the reservoir parameters includes: in, Oil saturation; Water saturation; RESD is resistivity logging data. PHIT stands for porosity. The relationship between the original oil saturation data, resistivity logging data, and porosity obtained from sealed core samples of glauconite sandstone.

6. The apparatus according to claim 5, characterized in that, The glauconite sandstone reservoir parameters include: actual clay content, actual quartz content, actual glauconite content, and porosity.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; A memory connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor implements the mineral reservoir evaluation method according to any one of claims 1 to 3 by executing the instructions stored in the memory.

8. A machine-readable storage medium, characterized in that, The machine contains machine instructions that, when executed on the machine, cause the machine to perform the mineral reservoir evaluation method according to any one of claims 1 to 3.

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