CN113756793A - Method, device and equipment for determining shale oil exploitation mode and readable storage medium - Google Patents
Method, device and equipment for determining shale oil exploitation mode and readable storage medium Download PDFInfo
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Abstract
The application relates to a method, a device, equipment and a storage medium for determining a shale oil exploitation mode, and relates to the field of shale oil exploration. The method comprises the following steps: receiving logging data of a target rock stratum, which are sent by a full-column acoustic logging device, wherein the logging data are used for indicating rock stratum properties of the target rock stratum; inputting the logging data into a logging data analysis model, and outputting to obtain a brittleness index of the target rock stratum; determining a seam network index of the target rock stratum according to the logging data and the brittleness index; and determining the mining mode of the target rock stratum according to the seam network index. According to the method, the logging data of the target rock stratum are obtained from the detection device, the brittleness index and the seam network index of the target rock stratum are determined, the mining mode of the target rock stratum is finally determined, the brittleness index is set and processed, the relative easy-to-break degree of the brittleness of the target rock stratum in the mining area is determined, the mining mode of the target rock stratum is determined according to the brittleness index, and the efficiency of determining the mining mode of the shale oil is improved.
Description
Technical Field
The application relates to the field of shale oil exploration, in particular to a method, a device, equipment and a readable storage medium for determining a shale oil exploitation mode.
Background
Shale oil refers to a petroleum resource contained in a shale layer system mainly composed of shale. Including oil in shale pores and fractures, and also including oil resources in tight carbonate or clastic adjacent layers and interbedded layers in shale layer systems. Generally, the development of effective shale oil is horizontal well and staged fracturing techniques.
Prior to the production of shale oil, an analysis of the formation properties of the production zone is required to determine whether the zone is suitable for production and in what manner. At present, the brittleness of the rock stratum is mostly used as the basis of the quality of the shale oil reservoir, and the fracturing property of the rock stratum is used as the basis of the exploitation mode.
However, in the related art, a quantitative evaluation method for the formation compressibility does not exist, and the determination of the mining mode is low in efficiency.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a readable storage medium for determining a shale oil exploitation mode, which can determine the quality of brittleness of a target rock stratum in an exploitation range through setting of a brittleness index, guide exploitation of shale oil according to the quality, and improve the determination efficiency of the shale oil exploitation mode. The technical scheme is as follows:
in one aspect, a method for determining a shale oil exploitation mode is provided, and the method comprises the following steps:
logging data of a target rock stratum sent by the receiving full-column acoustic logging device, wherein the logging data are used for indicating rock stratum properties of the target rock stratum, and the target rock stratum is a rock stratum to be mined in a mining area;
inputting the logging data into a logging data analysis model, and outputting to obtain a brittleness index of the target rock stratum, wherein the brittleness index is used for indicating the degree of easy fragmentation of the target rock stratum relative to other rock strata in the mining area, and the logging data analysis model is a machine learning model;
determining a seam network index of the target rock stratum through the logging data and the brittleness index, wherein the seam network index is used for indicating the compressibility of the target rock stratum in the mining area;
and determining the mining mode of the target rock stratum according to the seam network index.
In an alternative embodiment, inputting the logging data into a logging data analysis model, and outputting to obtain a brittleness index of the target formation, comprises:
obtaining the peak strain of each rock stratum in the mining area through the logging data to obtain the target peak strain of the target rock stratum, the maximum peak strain in the mining area and the minimum peak strain in the mining area; determining a peak strain index of the target formation from the target peak strain, the maximum peak strain, and the minimum peak strain;
acquiring a shearing and expanding angle of each rock stratum in the mining area through logging data to obtain a target shearing and expanding angle of the target rock stratum, a maximum shearing and expanding angle in the mining area and a minimum shearing and expanding angle in the mining area; determining the shear-expansion angle index of the target rock stratum through the target shear-expansion angle, the maximum shear-expansion angle and the minimum shear-expansion angle;
acquiring the Young modulus of each rock stratum in the mining area through the logging data, and acquiring the target Young modulus of the target rock stratum, the maximum Young modulus in the mining area and the minimum Young modulus in the mining area; determining a Young modulus index of the target rock stratum through the target Young modulus, the maximum Young modulus and the minimum Young modulus;
and carrying out weighted summation on the peak strain index, the shear expansion angle index and the Young modulus index to obtain the brittleness index.
In an alternative embodiment, the log data includes compressional moveout, rock density, reservoir porosity, and natural gamma values;
obtaining peak strain for each formation within the production zone from the log data, comprising:
acquiring the peak strain of each rock stratum through longitudinal wave time difference, rock density, reservoir porosity and natural gamma value;
obtaining a shear expansion angle for each formation in a production zone from log data, comprising:
acquiring the shear expansion angle of each rock stratum through longitudinal wave time difference, rock density and reservoir porosity;
obtaining a young's modulus for each formation within the production zone from the log data, comprising:
the young's modulus of each formation was obtained by longitudinal wave time difference and rock density.
In an alternative embodiment, the well log data further includes a maximum horizontal principal stress of the target formation, a minimum horizontal principal stress of the target formation, and an angle between a hydraulic fracture face and a natural fracture face in the target formation;
determining a fracture network index of the target formation through the well logging data and the brittleness index, wherein the fracture network index comprises:
determining a natural fracture influence factor of the target rock stratum through the maximum horizontal principal stress, the minimum horizontal principal stress and an included angle between a hydraulic fracture surface and a natural fracture surface in the target rock stratum, wherein the natural fracture influence factor is used for indicating the influence degree of a natural fracture on the rock stratum state of the target rock stratum;
and weighting the brittleness index through the natural crack influence factor to obtain the seam network index.
In an alternative embodiment, weighting the brittleness index by a natural fracture influencing factor to obtain a fracture network index further comprises:
determining an earth stress influence factor of the target rock stratum through the maximum horizontal principal stress and the minimum horizontal principal stress, wherein the earth stress influence factor is used for indicating the capability of the target rock stratum to deform under the influence of external stress;
weighting and summing the ground stress influence factor and the natural fracture influence factor to obtain a seam network coefficient;
and weighting the brittleness index through the sewing network coefficient to obtain the sewing network index.
In an optional embodiment, the logging data of the target formation further comprises an earth stress influence factor weight coefficient and a natural fracture influence factor weight coefficient;
weighting and summing the ground stress influence factor and the natural fracture influence factor to obtain a seam network coefficient, wherein the method comprises the following steps:
and taking the ground stress influence factor weight coefficient as a weight of the ground stress influence factor, taking the natural fracture influence factor weight coefficient as a weight of the natural fracture influence factor, and carrying out weighted summation on the ground stress influence factor and the natural fracture influence factor to obtain the seam network coefficient.
In an alternative embodiment, determining the production pattern for the target formation based on the seam crossing index comprises:
and determining a perforation opening position when the target rock stratum is mined according to the seam network index.
In another aspect, there is provided a shale oil exploitation method determination apparatus, the apparatus comprising:
the receiving module is used for receiving logging data of a target rock stratum, which are sent by the full-column acoustic logging device, wherein the logging data are used for indicating the rock stratum property of the target rock stratum, and the target rock stratum is a rock stratum to be mined in a mining area;
the determining module is used for determining a brittleness index of the target rock stratum through the logging data, and the brittleness index is used for indicating the fracture easiness degree of the target rock stratum relative to other rock strata in the mining area;
the determining module is further used for determining a seam network index of the target rock stratum through the logging data and the brittleness index, and the seam network index is used for indicating the compressibility of the target rock stratum in the mining area;
and the determining module is also used for determining the mining mode of the target rock stratum according to the seam network index.
In another aspect, a computer apparatus is provided, the apparatus comprising a processor and a memory, the memory having stored therein at least one instruction, the instruction being loaded and executed by the processor to implement the method of determining a shale oil recovery mode as any one of the above.
In another aspect, there is provided a computer readable storage medium having stored therein at least one instruction, at least one program, set of codes, or set of instructions, which is loaded and executed by a processor to perform a method of determining a shale oil recovery mode as any one of the above.
In another aspect, a computer program product is provided which, when run on a computer, causes the computer to perform a method of shale oil production approach determination as described in any of the embodiments of the present application above.
The beneficial effects brought by the technical scheme provided by the embodiment of the application at least comprise:
the method comprises the steps of obtaining logging data of a target rock stratum from a detection device, determining the brittleness index and the seam crossing index of the target rock stratum through the logging data, finally determining the mining mode of the target rock stratum according to the seam crossing index, determining the relative easy-to-break degree of the brittleness of the target rock stratum in a mining area through setting and processing the brittleness index, determining the mining mode of the target rock stratum according to the relative easy-to-break degree, and improving the efficiency of determining the mining mode of shale oil.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 illustrates a flow chart of a method of determining a shale oil production mode provided by an exemplary embodiment of the present application;
FIG. 2 is a flow chart illustrating a method for determining a frangibility index and a seam web index provided by an exemplary embodiment of the present application;
FIG. 3 illustrates a graphical representation of a brittleness index curve provided by an exemplary embodiment of the present application;
FIG. 4 illustrates a graphical representation of a ground stress factor-of-influence curve provided by an exemplary embodiment of the present application;
FIG. 5 illustrates a schematic diagram of a natural fracture factor-of-influence curve provided by an exemplary embodiment of the present application;
FIG. 6 illustrates a graphical representation of a seam mesh index curve provided by an exemplary embodiment of the present application;
FIG. 7 illustrates a flow chart of a method of shale oil production mode determination provided by an exemplary embodiment of the present application;
FIG. 8 is a block diagram illustrating a shale oil production mode determining apparatus according to an exemplary embodiment of the present application;
fig. 9 shows a schematic structural diagram of a server according to an exemplary embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
First, the terms referred to in the embodiments of the present application will be briefly described:
logging data: is data obtained by geological exploration of a mining area prior to the mining of a shale oil reservoir. The method for acquiring the logging data can be full-wave-train acoustic logging, and the method for acquiring the logging data is not limited in the application. The well log data is used to characterize geological properties of the production zone. Optionally, the well log data includes at least one of compressional moveout of the formation, rock density of the formation, reservoir porosity of the formation, and natural gamma values of the formation.
In one example, a server is connected to a full string acoustic logging device for placement within a production zone and transmitting acoustic waves to a shale layer within the production zone. By the method of sending sound waves with different frequencies and different directions and receiving subsequent sound waves and processing received sound wave signals through a sound wave processing model in a server, a logging data curve with different parameters as variables can be obtained.
Optionally, the logging data including the longitudinal wave time difference of the rock formation, the rock density of the rock formation, the reservoir porosity of the rock formation, and the natural gamma value of the rock formation may be used for performing one-sided characterization on the reservoir condition of the shale oil reservoir, but the reservoir quality of the shale oil reservoir and the exploitation difficulty of the reservoir cannot be comprehensively realized. In the related art, the brittleness of rock formations is generally used to represent the reservoir quality of shale reservoirs. At present, the oil deposit quality evaluation method of shale oil deposit which is applied more is a method for evaluating the brittleness of the shale oil deposit. The brittleness evaluation method is a method for calculating the brittleness of a rock stratum by using rock mechanical parameters, however, the related methods are established aiming at the mechanical parameters of rock minerals of the marine shale, and have obvious regional limitations and no representativeness. And the parameters of the calculation method provided by the related method are incomplete, and finally, the rock brittleness represented in a constant form and a non-uniform unit can only be obtained, and the number has no physical significance and also has no capability of guiding the actual exploitation situation.
Fig. 1 shows a flowchart of a method for determining a shale oil production mode according to an exemplary embodiment of the present application. For the explanation by taking the application of the method to a logging data processing server as an example, the method comprises the following steps:
Optionally, the full-string acoustic logging device is a logging data acquisition device disposed within the production area. Which collects logging data by sending different kinds of sound waves.
Optionally, the production zone is a zone determined to perform production work on shale oil. In determining the mining area, a surface area of the mining area, i.e., the surface area and a subsurface area of the surface area, may first be determined at the surface. In one example, after a surface region of a production zone is determined at the surface, the surface region and all rock formations within a depth range of 2000 meters below the surface region corresponding to the surface region are determined to be a production zone.
Optionally, depth is used as a criterion for dividing between rock formations. Optionally, the rock formation to be mined within a depth range in the mining area is determined as the target rock formation. In one example, after the surface area and the mining area with the depth range of 2000 meters underground corresponding to the surface area as rock formations are determined, the rock formations with the depth range of 1000 meters underground and the depth range corresponding to the depth range are selected as target rock formations. Optionally, rock formations in the depth range of 950 meters underground to 1050 meters underground are selected as the target rock formation. Optionally, the full-column acoustic logging device emits acoustic waves from the underground 950 depth, and the acoustic waves are reflected and refracted at the rock stratum 1050 meters deep below the ground and sent to the full-column acoustic logging device for acquiring logging data in the target rock stratum.
And after receiving the sound waves which are reflected and refracted and reach the full-column sound wave logging device again, the full-column sound wave logging device generates logging data of the target rock stratum. The well log data is indicative of formation properties of the target formation. In one example, the log data for the target formation includes at least one of a compressional wave moveout of the target formation, a rock density of the target formation, and a reservoir porosity of the target formation. Optionally, after the logging data is generated, the full-column acoustic logging device sends the logging data to the logging data processing server for subsequent data processing, or the full-column acoustic logging device periodically collects and updates the logging data of the target rock formation, and the logging data processing server periodically receives the logging data of the target rock formation from the full-column acoustic logging device.
And 102, inputting the logging data into a logging data analysis model, and outputting to obtain a brittleness index of the target rock stratum, wherein the brittleness index is used for indicating the fragile degree of the target rock stratum relative to other rock strata in the mining area.
Brittleness is used to indicate the property of a material that can break with only a small deformation under an external force, and optionally, the brittleness index is evaluated numerically for the degree of fracture of the target formation. In one example, the target formation has a brittleness index that is a fraction between 0 and 1, the closer the brittleness index is to 1 indicating a higher relative value of brittleness at the target formation within the production zone, the more brittle the target formation, and the closer the brittleness index is to 0 indicating a lower relative value of brittleness at the target formation within the production zone, the less brittle the target formation.
Optionally, the logging data processing server includes a logging data analysis model, and the logging data analysis model is a machine learning model. Optionally, the brittleness index is output by processing the log data as an input to obtain a brittleness index of the target formation. Or, the brittleness index is processed by the side of logging data as input quantity, and an intermediate parameter for direct calculation to obtain the brittleness index is output.
And 103, determining a seam network index of the target rock stratum through the logging data and the brittleness index, wherein the seam network index is used for indicating the crushable performance of the target rock stratum in the mining area.
In performing shale reservoir development work on a target formation, it is often necessary to fracture the target formation with a fracturing agent, the fracturability of the target formation indicating how difficult the target formation can be fractured by the fracturing agent. Because the fracturing performance of the target rock stratum cannot be comprehensively evaluated by the logging data of the single target rock stratum, the logging data needs to be processed by the logging data processing server to obtain the fracture network index of the target rock stratum, so that the fracturing difficulty of the target rock stratum is evaluated.
Optionally, the well logging data processing server includes a slotted network index model. The well logging data processing server receives well logging data sent by the full-column acoustic logging device, obtains the brittleness index of the target rock stratum through the brittleness index model, and then correspondingly inputs the well logging data corresponding to the target rock stratum and the brittleness index into the network fracture index model to output the network fracture index of the target rock stratum.
Optionally, the gap net index is a fraction between 0 and 1. A higher fracture network index indicates that the target formation is more likely to be fractured by the fracturing agent; the lower the fracture network index, the less likely the target formation is to be fractured by the fracturing agent.
And step 104, determining a mining mode for the target rock stratum according to the seam crossing index.
After the seam crossing index is determined, the logging data processing server determines the mining mode of the target rock stratum according to the seam crossing index. Optionally, when the target rock stratum is exploited, the exploitation efficiency of the shale oil reservoir in the target rock stratum needs to be improved by arranging the clustering perforator. Therefore, determining the mining mode of the target rock stratum comprises determining the perforation opening position when the target rock stratum is mined. In one example, after the perforations are formed, the mode for exploiting the shale oil reservoir comprises a first exploitation mode and a second exploitation mode, the first exploitation mode is selected when the seam crossing index is larger than a seam crossing index threshold, and the second exploitation mode is selected when the seam crossing index is smaller than or equal to the seam crossing index threshold.
In summary, according to the method provided by this embodiment, the well logging data of the target rock formation is obtained from the detection device, the brittleness index and the fracture network index of the target rock formation are determined according to the well logging data, and finally the mining mode for the target rock formation is determined according to the fracture network index.
Optionally, in the process of obtaining the brittleness index of the target rock formation, an intermediate parameter needs to be obtained, the brittleness index of the target rock formation is obtained through the obtained intermediate parameter, and then the seam network index of the target rock formation is determined. Fig. 2 is a schematic flow chart illustrating a method for determining a brittleness index and a seam crossing index according to an exemplary embodiment of the present application, which is described by way of example in the application of the method to a logging data server, and the method includes:
Optionally, the log data includes compressional moveout, rock density, reservoir porosity, and natural gamma values.
Optionally, if the longitudinal wave time difference is Δ tp and the transverse wave time difference is Δ ts, the logging data processing server may obtain the transverse wave time difference by using the longitudinal wave time difference according to the following formula:
Δts＝528.71×ln(Δtp)-2447.3
young's modulus is a physical quantity that describes the ability of a solid material to resist deformation, and in this application, Young's modulus may be dynamic Young's modulus, or Young's modulus may be static Young's modulus.
The method for processing the logging data by the logging data server to finally obtain the Young modulus of the target rock stratum is as follows:
the method for obtaining the dynamic Young modulus is shown as the following formula:
in the formula, rho is the rock density of the target rock stratum and is selected in kg/m3；VsSelecting the unit as m/s for the transverse wave velocity; vtSelecting the unit as m/s for longitudinal wave velocity; edThe dynamic Young's modulus is selected in MPa.
Alternatively, after obtaining the dynamic young's modulus of each target formation, the maximum young's modulus and the minimum young's modulus in the production zone may be determined.
Alternatively, the static young's modulus is obtained by the following formula:
Es＝1.757×Ed-21370
in the formula, EdFor dynamic Young's modulus, the units are selected to be MPa and EsThe static Young's modulus is selected in MPa.
Optionally, since the increase relationship between the dynamic young's modulus and the static young's modulus is positive correlation, when the maximum dynamic young's modulus is selected, the maximum static young's modulus is selected, and when the minimum dynamic young's modulus is selected, the minimum static young's modulus is selected.
The Young's modulus index is obtained by the following formula:
in the formula, EnThe young's modulus index of the target formation may be a dynamic young's modulus index or a static young's modulus index. E is the target Young's modulus, EminAt minimum Young's modulus, EmaxThe maximum young's modulus.
Shear swell angle is a physical quantity used to represent the rate of change of volume of a material during shearing.
The method for acquiring the shear expansion angle of the target rock stratum by processing the logging data and the intermediate parameters by the logging data server comprises the following steps:
the method for acquiring the shear-expansion angle of the target rock stratum is shown as the following formula:
wherein psi is the shearing expansion angle of the target rock stratum, and the selected unit is DEG;
selecting an internal friction angle of a target rock stratum with the unit of degree; sigmacSelecting the rock compressive strength of a target rock stratum in MPa; pcThe unit of the confining pressure is MPa.
The method for acquiring the rock compressive strength of the target rock stratum is shown as the following formula:
σc＝(0.0045+0.0035Vsh)Ed
wherein, VshThe shale content of the target rock stratum is a dimensionless value; edThe target rock formation is dynamic Young's modulus, and the unit is selected to be MPa.
The method for obtaining the shale content of the target rock stratum is shown as the following formula:
wherein, GCUR is an experience coefficient related to the age, and the value is taken according to the age time of geology. SH is the natural gamma relative value of the target formation.
The method for obtaining the natural gamma relative value of the target rock stratum is shown as the following formula:
wherein, GRmaxThe natural gamma value of the pure lithologic stratum is a dimensionless value; GRminThe natural gamma value of the pure argillaceous stratum is a dimensionless value; GR is the natural gamma value of the target formation.
The method for obtaining the internal friction angle of the target rock stratum is shown as the following formula:
wherein phi is the reservoir porosity of the target rock stratum and is decimal.
The method for acquiring the confining pressure of the target rock stratum is shown as the following formula:
h is the depth of a target rock stratum, and the selected unit is m; alpha is the effective stress coefficient of the target rock stratum and is a dimensionless value, when the shale content is greater than 0.8, the effective stress coefficient is 0.6, when the shale content is less than 0.2, the effective stress coefficient is 0.9, when 0.2< the shale content is less than 0.8, the method for obtaining the effective stress coefficient is shown as the following formula:
Ppselecting kN as the unit of pore pressure of a target rock stratum; rho is the rock density of the target rock stratum and has the unit of kg/m 3; g is the gravity coefficient.
After the shear-expansion angle of each target rock stratum is obtained, the maximum shear-expansion angle and the minimum shear-expansion angle in the mining area can be obtained.
Optionally, the shear expansion angle index is obtained by the following formula:
wherein psinIs the shear expansion angle index of the target rock stratum, psi is the target shear expansion angle, psiminIs the minimum shear expansion angle, psimaxThe maximum shear expansion angle.
Strain refers to the change in the volume or shape of an object due to an external or internal defect. The peak strain of the target formation is the maximum change that the target formation can produce due to an extrinsic or intrinsic defect.
The method for obtaining the peak strain of the target rock stratum is shown as the following formula:
εp＝(σcv-19.17)/13.02
wherein σcThe unit of KN and v is the Poisson's ratio of the target rock stratum, and is a dimensionless value.
The method for obtaining the Poisson's ratio of the target rock stratum is shown as the following formula:
after the peak strain of each target formation is obtained, the maximum peak strain and the minimum peak strain in the production zone may be obtained.
Alternatively, the peak strain index is obtained by the following formula:
wherein epsilonpnIs the peak strain index of the target formation; epsilonpIs the target peak strain; epsilonpmaxIs the maximum peak strain; epsilonpminIs the minimum peak strain.
And 204, carrying out weighted summation on the peak strain index, the shear expansion angle index and the Young modulus index to obtain the brittleness index of the target rock stratum.
Optionally, the brittleness index of the target rock formation is obtained by the following formula:
BI＝0.262En+0.353ψn+0.385εpn
wherein, BINamely the brittleness index of the target rock stratum, the first weight corresponding to the peak strain index is 0.262, the second weight corresponding to the shear-expansion angle index is 0.353, and the weight corresponding to the Young modulus index is 0.385. Optionally, the weight is obtained by inputting the logging data into a logging data analysis model, and the logging data analysis model is a machine learning model. Optionally, the input quantity of the logging data analysis model is logging data of the target rock formation, and the output quantity is the first weight, the second weight and the third weight.
Alternatively, the logging data processing server may output the young's modulus index, the peak strain index, and the shear expansion angle index by inputting the logging data into the intermediate parametric model. And inputting the intermediate parameter into a brittleness index model, and outputting to obtain a brittleness index.
Alternatively, in the logging data processing server, the brittleness index processed from the logging data of each target formation in the production zone may be represented in a graph in the form of a brittleness index curve. In one example, FIG. 3 illustrates a graphical representation of a brittleness index curve provided by an exemplary embodiment of the present application. Please refer to fig. 3. The abscissa of the graph is a numerical value 301 of the brittleness index, the ordinate is a depth 302, according to the numerical value 301 and the depth 302 of the brittleness index, a brittleness index curve generated by overlapping the brittleness index of each target rock stratum in the mining area can be obtained, and the brittleness index of the target rock stratum can be determined according to the depth value.
And step 205, determining the ground stress influence factor of the target rock stratum according to the maximum horizontal principal stress, the minimum horizontal principal stress and the included angle between the hydraulic fracture surface and the natural fracture surface in the target rock stratum.
The ground stress influence factor of the target formation is used for indicating the capability of the target formation to deform under the influence of external stress. The method for acquiring the ground stress influence factor is shown as the following formula:
in the formula, SIIs a ground stress influence factor and is a dimensionless value; sigmaHSelecting the maximum horizontal principal stress in the target rock stratum in MPa; sigmahSelecting the minimum horizontal principal stress in the target rock stratum in MPa; epsilonHConstructing a coefficient, which is an empirical value, for the maximum horizontal principal stress in the target rock formation; delta sigmamThe maximum value of the block horizontal stress difference of the target rock stratum is shown in MPa.
Optionally, the maximum horizontal principal stress and the minimum horizontal principal stress are obtained by the following formulas:
in the formula, E is the elastic modulus of stratum rock in a fracturing target rock interval, and the selected unit is MPa; gamma is a minimum horizontal principal stress configuration coefficient and is a dimensionless value.
FIG. 4 illustrates a graphical representation of a ground stress factor-of-influence curve provided by an exemplary embodiment of the present application. Referring to fig. 4, the abscissa of the graph is the value 401 of the ground stress influence factor, and the ordinate is the depth 402, according to the value 401 and the depth 402 of the ground stress influence factor, a ground stress influence factor curve generated by superimposing the ground stress influence factors of each target rock formation in the mining area can be obtained, and the ground stress influence factor of the target rock formation can be determined according to the depth value.
And step 206, determining the natural fracture influence factor of the target rock stratum through the maximum horizontal principal stress, the minimum horizontal principal stress and the included angle between the hydraulic fracture surface and the natural fracture surface in the target rock stratum.
The natural fracture impact factor of the target formation indicates a degree of impact of the natural fracture on the formation state of the target formation. The method for acquiring the natural fracture influence factor is shown in the following formula:
in the formula, FnIs a natural crack influence factor and is a dimensionless value; theta is the angle between the hydraulic fracture face and the natural fracture face, in degrees, as described above, sigmaHAnd σhMaximum principal stress and minimum principal stress in the target rock formation, respectively, in units of MPa, σnmThe difference between the maximum principal stress and the minimum principal stress is given in MPa.
FIG. 5 illustrates a schematic diagram of a natural fracture factor of influence curve provided by an exemplary embodiment of the present application. Referring to fig. 5, the abscissa of the graph is the value 501 of the natural fracture influence factor, and the ordinate is the depth 502, according to the value 501 and the depth 502 of the natural fracture influence factor, a natural fracture influence factor curve generated by overlapping the natural fracture influence factors of each target rock formation in the mining area can be obtained, and according to the depth value, the natural fracture influence factor of the target rock formation can be determined.
And step 207, weighting and summing the ground stress influence factor and the natural fracture influence factor to obtain a seam network coefficient.
And step 208, weighting the brittleness index through the sewing network coefficient to obtain the sewing network index.
Optionally, the web sewing index is obtained in the following formula:
FI＝BI(w4Fn+w5SI)
in the formula, the weight coefficient of the natural fracture influence factor is a dimensionless value; the natural fracture influence factor weight coefficient and the ground stress influence factor weight coefficient are directly obtained logging data; fIIs a stitch index and is a dimensionless value. Alternatively, (w)4Fn+w5SI) The seam network coefficient is obtained by weighting and summing the ground stress influence factor and the natural fracture influence factor by taking the ground stress influence factor weight coefficient as the weight of the ground stress influence factor and taking the natural fracture influence factor weight coefficient as the weight of the natural fracture influence factor. And after the seam network coefficient is obtained, weighting and correcting the brittleness index through the seam network coefficient, and finally obtaining the seam network index of the target rock stratum.
FIG. 6 illustrates a graphical representation of a seam mesh index profile provided by an exemplary embodiment of the present application. Please refer to fig. 6. The abscissa of the graph is a seam network index 601, the ordinate is a depth 602, a seam network index curve generated by overlapping seam network indexes of each target rock stratum in a mining area can be obtained according to the seam network index 601 and the depth 602, and the seam network index of the target rock stratum can be determined according to the depth value.
In summary, according to the method provided by this embodiment, the brittleness index and the seam crossing index of each target rock stratum are obtained by obtaining the logging data and processing the logging data, and the brittleness index and the seam crossing index of the target rock stratum are quantitatively and comprehensively determined, so that the partitioning efficiency of shale oil exploitation is improved.
Fig. 7 shows a flowchart of a method for determining a shale oil production mode according to an exemplary embodiment of the present application, which is described by way of example as being applied to a logging data processing server, and the method includes:
Optionally, the full-column acoustic logging device is disposed on the target formation, or the full-column acoustic logging device is disposed at another position, and logging data of the target formation is acquired through acoustic detection of the target formation.
And step 702, acquiring the Young modulus index, the shear-expansion angle index and the peak strain index of the target rock stratum through the logging data.
The Young modulus index, the shear-expansion angle index and the peak strain index are all intermediate parameters in the process of obtaining the brittleness index of the target rock stratum from logging data. The specific methods for obtaining the young's modulus index, the shear angle index, and the peak strain index are shown in steps 301 to 303.
And 703, carrying out weighted summation on the peak strain index, the shear-expansion angle index and the Young modulus index to obtain the brittleness index of the target rock stratum.
Optionally, the logging data server stores a first weight, a second weight, and a third weight corresponding to the young modulus index, the shear-expansion angle index, and the peak strain index, and the brittleness index of the target rock formation can be obtained by performing weighted summation on the three parameters. Optionally, the first weight, the second weight, and the third weight are three weights obtained by inputting logging data into the logging data analysis model.
And 704, acquiring the ground stress influence factor of the target rock stratum and the natural fracture influence factor of the target rock stratum through the logging data.
Alternatively, the earth stress influence factor of the target formation and the natural fracture influence factor of the target formation may be obtained as shown in step 205 and step 206, respectively.
And 706, weighting the brittleness index through the sewing network coefficient to obtain the sewing network index.
Optionally, as shown in step 208, the brittleness index is weighted by a seam network index, and a seam network index is obtained that may indicate the crushability of the target formation.
And step 707, determining a mining mode of the target rock stratum according to the seam network index.
In one example, determining a perforation opening position when the target rock stratum is mined according to the seam network index; in another example, the fracture pattern and the perforation clustering pattern of the target formation are determined from the fracture network index.
In summary, according to the method provided by this embodiment, the well logging data of the target rock formation is obtained from the detection device, the brittleness index and the fracture network index of the target rock formation are determined according to the well logging data, and finally the mining mode for the target rock formation is determined according to the fracture network index.
Fig. 8 is a block diagram showing a structure of a shale oil exploitation mode determination apparatus according to an exemplary embodiment of the present application, the apparatus including:
the receiving module 801 is used for receiving logging data of a target rock stratum sent by the full-column acoustic logging device, wherein the logging data is used for indicating rock stratum properties of the target rock stratum, and the target rock stratum is a rock stratum to be mined in a mining area;
the input module 802 is used for inputting the logging data into a logging data analysis model, outputting the logging data to obtain a brittleness index of the target rock stratum, wherein the brittleness index is used for indicating the fracture easiness degree of the target rock stratum relative to other rock strata in the mining area, and the logging data analysis model is a machine learning model;
a determining module 803, configured to determine a brittleness index of the target rock formation according to the log data, where the brittleness index is used to indicate a fracture susceptibility of the target rock formation relative to other rock formations in the mining area;
determining a seam network index of the target rock stratum through the logging data and the brittleness index, wherein the seam network index is used for indicating the compressibility of the target rock stratum in the mining area;
and determining the mining mode of the target rock stratum according to the seam network index.
In an optional embodiment, the apparatus further includes an obtaining module 804, configured to obtain a peak strain of each rock formation in the production region from the log data, and obtain a target peak strain of the target rock formation, a maximum peak strain in the production region, and a minimum peak strain in the production region; determining a peak strain index of the target formation from the target peak strain, the maximum peak strain, and the minimum peak strain;
acquiring a shearing and expanding angle of each rock stratum in the mining area through logging data to obtain a target shearing and expanding angle of the target rock stratum, a maximum shearing and expanding angle in the mining area and a minimum shearing and expanding angle in the mining area; determining the shear-expansion angle index of the target rock stratum through the target shear-expansion angle, the maximum shear-expansion angle and the minimum shear-expansion angle;
acquiring the Young modulus of each rock stratum in the mining area through the logging data, and acquiring the target Young modulus of the target rock stratum, the maximum Young modulus in the mining area and the minimum Young modulus in the mining area; determining a Young modulus index of the target rock stratum through the target Young modulus, the maximum Young modulus and the minimum Young modulus;
the input module 802 is further configured to input the logging data into the logging data analysis model, and output a first weight corresponding to the peak strain index, a second weight corresponding to the shear-expansion angle index, and a third weight corresponding to the young modulus index;
the device further comprises a summing module 805, which is used for weighting and summing the peak value strain index corresponding to the first weight, the shear expansion angle index corresponding to the second weight, and the Young modulus index corresponding to the third weight to obtain the brittleness index of the target rock stratum.
In an alternative embodiment, the log data includes compressional moveout, rock density, reservoir porosity, and natural gamma values;
an obtaining module 804, configured to obtain a peak strain of each rock stratum through a longitudinal wave time difference, a rock density, a reservoir porosity, and a natural gamma value;
acquiring the shear expansion angle of each rock stratum through longitudinal wave time difference, rock density and reservoir porosity;
the young's modulus of each formation was obtained by longitudinal wave time difference and rock density.
In an alternative embodiment, the well log data further includes a maximum horizontal principal stress of the target formation, a minimum horizontal principal stress of the target formation, and an angle between a hydraulic fracture face and a natural fracture face in the target formation;
a determining module 803, configured to determine a natural fracture influence factor of the target rock formation according to the maximum horizontal principal stress, the minimum horizontal principal stress, and an included angle between a hydraulic fracture surface in the target rock formation and a natural fracture surface, where the natural fracture influence factor is used to indicate an influence degree of a natural fracture on a rock formation state of the target rock formation;
the apparatus further includes a weighting module 806 for weighting the brittleness index by the natural fracture influencing factor to obtain a seam network index.
In an optional embodiment, the determining module 802 is configured to determine an earth stress influence factor of the target rock formation according to the maximum level principal stress and the minimum level principal stress, where the earth stress influence factor is used to indicate an ability of the target rock formation to deform under the influence of an external stress;
a summing module 805, configured to sum the ground stress influence factor and the natural fracture influence factor in a weighted manner to obtain a seam network coefficient;
and a weighting module 806, configured to weight the brittleness index according to the seam network coefficient to obtain a seam network index.
In an optional embodiment, the logging data of the target formation further comprises an earth stress influence factor weight coefficient and a natural fracture influence factor weight coefficient;
and a summing module 805, configured to take the ground stress influence factor weight coefficient as a weight of the ground stress influence factor, take the natural fracture influence factor weight coefficient as a weight of the natural fracture influence factor, and perform weighted summation on the ground stress influence factor and the natural fracture influence factor to obtain a seam network coefficient.
In an alternative embodiment, the determining module 803 is configured to determine the perforation opening position when the target rock formation is mined according to the seam crossing index.
It should be noted that: the device for determining the shale oil exploitation mode provided by the above embodiment is only exemplified by the division of the above functional modules, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the apparatus is divided into different functional modules, so as to complete all or part of the above description.
The application also provides a server which comprises a processor and a memory, wherein at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to realize the shale oil exploitation mode determination method provided by the various method embodiments. It should be noted that the server may be a server as provided in fig. 9 below.
Referring to fig. 9, a schematic structural diagram of a server according to an exemplary embodiment of the present application is shown. Specifically, the method comprises the following steps: the server 900 includes a Central Processing Unit (CPU) 901, a system Memory 904 including a Random Access Memory (RAM) 902 and a Read-Only Memory (ROM) 903, and a system bus 905 connecting the system Memory 104 and the CPU 901. The server 900 also includes a basic Input/Output (I/O) System 906, which facilitates the transfer of information between devices within the computer, and a mass storage device 907 for storing an operating System 913, application programs 914, and other program modules 915.
The basic input/output system 906 includes a display 908 for displaying information and an input device 909 such as a mouse, keyboard, etc. for user input of information. Wherein a display 908 and an input device 909 are connected to the central processing unit 901 through an input-output controller 910 connected to the system bus 905. The basic input/output system 906 may also include an input/output controller 910 for receiving and processing input from a number of other devices, such as a keyboard, mouse, or electronic stylus. Similarly, input-output controller 910 also provides output to a display screen, a printer, or other type of output device.
The mass storage device 907 is connected to the central processing unit 901 through a mass storage controller (not shown) connected to the system bus 905. The mass storage device 907 and its associated computer-readable media provide non-volatile storage for the server 900. That is, mass storage device 907 may include a computer-readable medium (not shown) such as a hard disk or CD-ROM drive.
Without loss of generality, computer readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes RAM, ROM, Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), flash Memory or other solid state Memory technology, CD-ROM, Digital Versatile Disks (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices. Of course, those skilled in the art will appreciate that computer storage media is not limited to the foregoing. The system memory 904 and mass storage device 907 described above may be collectively referred to as memory.
The memory stores one or more programs configured to be executed by the one or more central processing units 901, the one or more programs containing instructions for implementing the method for determining a shale oil production approach described above, the central processing unit 901 executing the one or more programs implementing the method for determining a shale oil production approach provided by the various method embodiments described above.
The server 900 may also operate as a remote computer connected to a network via a network, such as the internet, in accordance with various embodiments of the present application. That is, the server 900 may be connected to the network 912 through the network interface unit 911 connected to the system bus 905, or the network interface unit 911 may be used to connect to other types of networks or remote computer systems (not shown).
The memory further comprises one or more programs, the one or more programs are stored in the memory, and the one or more programs comprise steps executed by the server for carrying out the shale oil exploitation mode determination method provided by the embodiment of the application.
The embodiment of the application also provides a computer program product, and when the computer program product runs on a computer, the computer is enabled to execute the method for determining the shale oil exploitation mode provided by the method embodiments.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, which may be a computer readable storage medium contained in a memory of the above embodiments; or it may be a separate computer-readable storage medium not incorporated in the terminal. The computer readable storage medium has stored therein at least one instruction, at least one program, set of codes, or set of instructions that is loaded and executed by a processor to perform the method of determining a shale oil production mode as described above.
Optionally, the computer-readable storage medium may include: a Read Only Memory (ROM), a Random Access Memory (RAM), a Solid State Drive (SSD), or an optical disc. The Random Access Memory may include a resistive Random Access Memory (ReRAM) and a Dynamic Random Access Memory (DRAM). The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
The present application is intended to cover various modifications, alternatives, and equivalents, which may be included within the spirit and scope of the present application.
Claims (10)
1. A shale oil exploitation mode determination method is applied to a logging data processing server, and comprises the following steps:
receiving logging data of a target rock stratum sent by full-column acoustic logging equipment, wherein the logging data are used for indicating rock stratum properties of the target rock stratum, and the target rock stratum is a rock stratum to be mined in a mining area;
inputting the logging data into a logging data analysis model, and outputting to obtain a brittleness index of the target rock stratum, wherein the brittleness index is used for indicating the fragile degree of the target rock stratum relative to other rock strata in the mining area, and the logging data analysis model is a machine learning model;
determining a fracture network index of the target formation from the logging data and the brittleness index, the fracture network index being indicative of the crushable nature of the target formation within the production zone;
and determining the mining mode of the target rock stratum according to the seam network index.
2. The method of claim 1, wherein inputting the well log data into a well log data analysis model and outputting a brittleness index of the target formation comprises:
obtaining the peak strain of each rock stratum in the mining area through the logging data, and obtaining the target peak strain of the target rock stratum, the maximum peak strain in the mining area and the minimum peak strain in the mining area; determining a peak strain index for the target formation from the target peak strain, the maximum peak strain, and the minimum peak strain;
acquiring a shearing and expanding angle of each rock stratum in the mining area through the logging data to obtain a target shearing and expanding angle of the target rock stratum, a maximum shearing and expanding angle in the mining area and a minimum shearing and expanding angle in the mining area; determining a shear expansion angle index of the target rock formation through the target shear expansion angle, the maximum shear expansion angle and the minimum shear expansion angle;
acquiring the Young modulus of each rock stratum in the mining area through the logging data to obtain a target Young modulus of the target rock stratum, a maximum Young modulus in the mining area and a minimum Young modulus in the mining area; determining a Young's modulus index of the target rock formation from the target Young's modulus, the maximum Young's modulus, and the minimum Young's modulus;
inputting the logging data into the logging data analysis model, and outputting to obtain a first weight corresponding to the peak strain index, a second weight corresponding to the shear-expansion angle index and a third weight corresponding to the Young modulus index;
and weighting and summing the peak strain index corresponding to the first weight, the shear expansion angle index corresponding to the second weight and the Young modulus index corresponding to the third weight to obtain the brittleness index of the target rock stratum.
3. The method of claim 2, wherein the well log data comprises compressional moveout, rock density, reservoir porosity, and natural gamma values;
the obtaining peak strain for each formation within the production zone from the well log data comprises:
obtaining the peak strain of each rock formation through the compressional moveout, the rock density, the reservoir porosity, and the natural gamma value;
the obtaining of the shear expansion angle of each rock formation in the production area from the well log data comprises:
acquiring the shear expansion angle of each rock stratum through the longitudinal wave time difference, the rock density and the reservoir porosity;
the obtaining of the Young's modulus of each rock formation within the production zone from the well log data comprises:
and acquiring the Young modulus of each rock stratum through the longitudinal wave time difference and the rock density.
4. The method of any of claims 1 to 3, wherein the well log data further comprises a maximum horizontal principal stress of the target formation, a minimum horizontal principal stress of the target formation, and an angle between a hydraulic fracture face and a natural fracture face in the target formation;
determining a fracture network index of the target formation from the well log data and the brittleness index, comprising:
determining a natural fracture influence factor of the target rock stratum according to the maximum horizontal principal stress, the minimum horizontal principal stress and an included angle between the hydraulic fracture surface and a natural fracture surface in the target rock stratum, wherein the natural fracture influence factor is used for indicating the influence degree of natural fractures on the rock stratum state of the target rock stratum;
and weighting the brittleness index through the natural fracture influencing factor to obtain the seam network index.
5. The method of claim 4, wherein the weighting the brittleness index by the natural fracture influencing factor to obtain the seam network index comprises:
determining an earth stress influence factor of the target rock stratum according to the maximum horizontal principal stress and the minimum horizontal principal stress, wherein the earth stress influence factor is used for indicating the capability of the target rock stratum to deform under the influence of external stress;
weighting and summing the ground stress influence factor and the natural fracture influence factor to obtain the seam network coefficient;
and weighting the brittleness index through the seam network coefficient to obtain the seam network index.
6. The method of claim 5, wherein the log data of the target formation further comprises an earth stress influence factor weight coefficient and a natural fracture influence factor weight coefficient;
the weighting and summing the ground stress influence factor and the natural fracture influence factor to obtain the seam network coefficient comprises:
and taking the ground stress influence factor weight coefficient as a weight of the ground stress influence factor, taking the natural fracture influence factor weight coefficient as a weight of the natural fracture influence factor, and carrying out weighted summation on the ground stress influence factor and the natural fracture influence factor to obtain the seam network coefficient.
7. The method of any of claims 1 to 3, wherein determining the production pattern for the target formation from the seam crossing index comprises:
and determining a perforation opening position when the target rock stratum is mined according to the seam network index.
8. A shale oil recovery mode determining apparatus, the apparatus comprising:
the system comprises a receiving module, a processing module and a processing module, wherein the receiving module is used for receiving logging data of a target rock stratum sent by a full-column acoustic logging device, the logging data is used for indicating rock stratum properties of the target rock stratum, and the target rock stratum is a rock stratum to be exploited in an exploitation area;
the input module is used for inputting the logging data into a logging data analysis model and outputting the obtained brittleness index of the target rock stratum, the brittleness index is used for indicating the fracture easiness degree of the target rock stratum relative to other rock strata in the mining area, and the logging data analysis model is a machine learning model;
the determining module is further used for determining a seam network index of the target rock formation through the logging data and the brittleness index, wherein the seam network index is used for indicating the fracturing capability of the target rock formation in the mining area;
the determining module is further used for determining a mining mode of the target rock stratum according to the seam crossing index.
9. A computer device comprising a processor and a memory, said memory having stored therein at least one instruction, said instruction being loaded and executed by said processor to implement a method of shale oil production mode determination as claimed in any of claims 1 to 7.
10. A computer readable storage medium having stored therein at least one instruction, at least one program, set of codes, or set of instructions, which is loaded and executed by a processor to carry out a method of determining a shale oil production mode according to any of claims 1 to 7.
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| CN106547034A (en) * | 2016-11-09 | 2017-03-29 | 西南石油大学 | A kind of method for calculating compact reservoir rock brittleness index |
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| CN104775810A (en) * | 2015-03-03 | 2015-07-15 | 西南石油大学 | Method for evaluating compressibility of shale gas reservoir |
| CN106547034A (en) * | 2016-11-09 | 2017-03-29 | 西南石油大学 | A kind of method for calculating compact reservoir rock brittleness index |
| CN106874544A (en) * | 2017-01-05 | 2017-06-20 | 西南石油大学 | A kind of geology characterizing method of shale reservoir reconstruction volume |
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