CN115685371A - Multi-factor quantitative evaluation method for favorable-to-reserve area of continental-facies hydrocarbon-containing basin - Google Patents
Multi-factor quantitative evaluation method for favorable-to-reserve area of continental-facies hydrocarbon-containing basin Download PDFInfo
- Publication number
- CN115685371A CN115685371A CN202110842222.8A CN202110842222A CN115685371A CN 115685371 A CN115685371 A CN 115685371A CN 202110842222 A CN202110842222 A CN 202110842222A CN 115685371 A CN115685371 A CN 115685371A
- Authority
- CN
- China
- Prior art keywords
- reservoir
- evaluation
- parameter
- fault
- favorable
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 54
- 239000004215 Carbon black (E152) Substances 0.000 title claims abstract description 25
- 229930195733 hydrocarbon Natural products 0.000 title claims abstract description 25
- 150000002430 hydrocarbons Chemical class 0.000 title claims abstract description 18
- 238000011158 quantitative evaluation Methods 0.000 title claims abstract description 16
- 208000035126 Facies Diseases 0.000 title description 3
- 238000011156 evaluation Methods 0.000 claims abstract description 65
- 238000011160 research Methods 0.000 claims abstract description 32
- 230000002349 favourable effect Effects 0.000 claims abstract description 31
- 238000004364 calculation method Methods 0.000 claims abstract description 23
- 238000010606 normalization Methods 0.000 claims abstract description 21
- 230000008569 process Effects 0.000 claims abstract description 21
- 238000012545 processing Methods 0.000 claims abstract description 14
- 238000012216 screening Methods 0.000 claims abstract description 5
- 239000004576 sand Substances 0.000 claims description 36
- 238000010586 diagram Methods 0.000 claims description 16
- 239000011435 rock Substances 0.000 claims description 14
- 230000015572 biosynthetic process Effects 0.000 claims description 12
- 238000009825 accumulation Methods 0.000 claims description 7
- 238000010276 construction Methods 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000004321 preservation Methods 0.000 claims description 4
- 230000009286 beneficial effect Effects 0.000 claims description 3
- 125000001183 hydrocarbyl group Chemical group 0.000 claims 7
- 238000009933 burial Methods 0.000 claims 2
- 238000005553 drilling Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 230000000694 effects Effects 0.000 description 3
- 238000012854 evaluation process Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 239000004615 ingredient Substances 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 230000019771 cognition Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000013506 data mapping Methods 0.000 description 1
- 238000000556 factor analysis Methods 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Landscapes
- Geophysics And Detection Of Objects (AREA)
Abstract
A multi-factor quantitative evaluation method for a land-phase hydrocarbon-containing basin favorable-to-reservoir area belongs to the technical field of comprehensive evaluation of oil and gas resources. The method takes multiple geological factors of six factors of 'birth, storage, covering, transportation, circling and protection' of the oil-gas reservoir as research cores, establishes a dynamic process of the oil-gas reservoir, assigns weights to geological parameters by taking the found oil reservoir as calibration, and realizes a quantitative evaluation calculation process of a reservoir favorable zone through a computer algorithm. The method comprises the following steps: 1) Defining the oil and gas reservoir forming main control factors; 2) Screening main storage control parameters; 3) Parameter gridding and normalization processing; 4) Giving each parameter a corresponding weighting coefficient; 5) Calculating the favorable area by multi-parameter superposition; 6) Judging whether the evaluation result meets the actual geological condition or not by utilizing the known oil-containing zone distribution; 7) If not, readjusting the weight value or the normalization parameter until the actual geological condition is met; 8) And outputting a calculation result, and judging the Tibetan favorable zone according to the percentile value.
Description
Technical Field
The invention relates to the technical field of comprehensive evaluation of oil and gas resources, in particular to a method for quantitatively evaluating a favorable reservoir formation zone of a fractured lake basin by multiple geological factors.
Background
The gathering of oil and gas into a reservoir in a land basin is a dynamic and complex geological process, and relates to a lot of geological factors, mainly relating to the research of six geological aspects, namely, the six elements 'birth, storage, cover, circle, transportation and protection' of the oil and gas reservoir, and how to effectively guide the beneficial reservoir area for exploring oil and gas is always a problem concerned by geologists. In many cases, the conventional method for evaluating the zonal accumulation is to stack a plurality of accumulation elements on a plane and qualitatively evaluate the accumulation zones in the research area. Specifically, parameters of each accumulation element are selected to carry out data mapping respectively, then geological maps are overlapped manually, and the favorable area is analyzed manually and qualitatively. The evaluation process is long in time consumption and low in prediction precision, and has higher requirements on the scientific research level of researchers.
With continuous exploration and more abundant data, a fast and efficient method capable of fully utilizing the multidisciplinary data of a research area and organically combining the multidisciplinary data to quantitatively and accurately predict an oil and gas reservoir zone of the research area is urgently needed.
Disclosure of Invention
The invention aims to overcome the problems in the background technology and provides a multi-factor quantitative evaluation method for a favorable-to-reserve-area of a terrestrial hydrocarbon-containing basin.
A land-based hydrocarbon-containing basin favorable-to-reservoir-area multi-factor quantitative evaluation method comprises the following steps:
step 1, defining the main control factors of oil and gas reservoir formation: comprehensive analysis is carried out on six elements of 'living, storage, covering, enclosing, transportation and preservation' of the research area to form understanding of the reservoir formation rule of the research area and to determine the main control factors of the oil and gas reservoir formation;
step 2, screening main reservoir control parameters: determining main reservoir control parameters according to the clear oil and gas reservoir forming main control factors;
step 3, parameter gridding and normalization processing: determining main reservoir control parameters, collecting corresponding main reservoir control parameter data, gridding the determined main reservoir control parameter data to form a parameter data volume, and then carrying out dimensionless normalization to obtain a plurality of gridded and normalized three-dimensional non-dimensionless parameter data volumes;
step 4, endowing each dimensionless parameter data volume with a corresponding weighting coefficient: the found oil reservoir is used as calibration, and each three-dimensional dimensionless parameter data volume is given a certain weighting coefficient according to the importance and the analysis result of the reservoir forming main control factors;
step 6, verifying the evaluation result: judging whether the evaluation result meets the actual geological condition or not by using the known favorable ingredient region band, if not, readjusting the weight value or performing normalization again until the actual geological condition is met, and determining a final evaluation score;
and 7, obtaining a final evaluation conclusion: and judging the region with high evaluation score as a Tibetan favorable zone according to the percentile score.
Preferably, the step 3 of collecting the corresponding main storage control parameter data is as follows: the evaluation related parameters of the hydrocarbon source rock comprise thickness, oil drainage strength and the like; reservoir evaluation related parameters include thickness, porosity, sand-to-ground ratio, etc.; the related parameters of the cover layer evaluation comprise thickness and the like; the trap effectiveness evaluation related parameters comprise a sand fracture ratio (vertical fault distance/sand layer thickness statistically calculated by a structural diagram) and the like; the oil gas storage conditions (cover layer damage degree, fault conditions and the like) comprise a cover breaking ratio (vertical fault distance/cover layer thickness statistically calculated by a structural diagram) and the like;
preferably, the parameter gridding processing process in step 3 is as follows: calling a gridding function of software with a three-dimensional data volume gridding function to perform plane gridding processing on the collected main storage control parameter data one by one to form a plurality of parameter data volumes;
preferably, the software with the three-dimensional data volume gridding function is a double fox geological mapping system or Surfer software and the like;
preferably, the process of performing dimensionless normalization processing on the parameter data volume after gridding each main reservoir control parameter comprises: according to the geological parameter characteristics of the research area, the parameter data volume after gridding is scored, different evaluation coefficients are given to different parameters within different ranges, if the evaluation coefficient range of a certain parameter is [ a, b ]]Given a corresponding parameter value range of [ Z ] 1 ,Z 2 ]When the parameter value is Z epsilon [ Z ∈ [ ] 1 ,Z 2 ]Then, the normalized parameter value Z' is:
the normalized parameter values are subjected to percentage system, namely the normalized parameter values are multiplied by 100, so that the evaluation significance is kept conveniently, and a plurality of gridded and normalized three-dimensional parameter data bodies are finally obtained.
The calculation of the vertical fault-distance needs to utilize the collected construction diagram to further calculate a sand fracture ratio parameter and a cover fracture ratio parameter, and a specific formula and an algorithm are used in the process of calculating the vertical fault-distance in a statistical manner; the sand break ratio is the ratio of the vertical fault distance to the sand layer thickness calculated by the statistics of a construction diagram; the cap break ratio is the ratio of the vertical distance to the cap layer thickness statistically calculated from the formation map.
Preferably, the vertical pitch statistic process is as follows:
(1) Extracting a fault plane distribution range (fault polygon data) and a structural map depth or altitude contour line from the vector structural map, and encrypting data points according to a certain precision requirement;
(2) For each point on the encrypted fault polygon, searching two corresponding nearest points of the other disc of the same fault, making a perpendicular line of the other two nearest points from the point, wherein the distance between the point and the foot is the horizontal fault distance, and the absolute value of the difference between the point and the foot in the depth or altitude value of the projection of the construction diagram is the vertical fault distance;
(3) And (4) performing the same calculation on each point on the fault polygon after plane encryption to obtain plane distribution data of the vertical fault distance.
Preferably, the calculation process and method of the horizontal fault distance comprises the following steps:
one point P outside the straight line 1 (x 1 ,y 1 ) Belongs to a certain point on a fault disc, and a straight line Ax + By + C =0 (A) 2 +B 2 Not equal to 0) is the distance P on the other disk of the slice 1 (x 1 ,y 1 ) Two points (P) with the closest points 2 (x 2 ,y 2 ),P 3 (x 3 ,y 3 ) ) line on, point P 1 (x 1 ,y 1 ) The distance formula to the above line is used to calculate the horizontal intercept d:
for a certain point on a fault disc, judging whether the corresponding point of another disc is judged by a vector method: the vector is directional, and when a certain point is judged to be on the left side and the right side of the straight line, the left direction and the right direction are opposite to the advancing direction, so long as the advancing direction is specifiedThen, it can be known that the three points P on the left and right, defining plane 1 (x 1 ,y 1 ),P 2 (x 2 ,y 2 ),P 3 (x 3 ,y 3 ) Three points form a triangle, the area is:
when P is 1 ,P 2 ,P 3 S is positive in counterclockwise direction, P is positive 1 ,P 2 ,P 3 Clockwise S is negative, let the start of the vector be P 2 End point is P 3 And, the judged point is P 1 If S (P) 1 ,P 2 ,P 3 ) Is positive, then P 1 In the vector P 2 P 3 Left side of, if S (P) 1 ,P 2 ,P 3 ) Is negative, then P 1 In the vector P 2 P 3 Right side of if S (P) 1 ,P 2 ,P 3 ) Is 0, then P 1 On a straight line P 2 P 3 The above step (1); corresponding points on two disks of the fault can be judged according to the data so as to avoid selecting the nearest two points as the points on the same disk of the fault.
Preferably, the statistical process of the sand break ratio is as follows: and respectively gridding the obtained plane distribution data of the vertical fault-distance and the plane distribution data of the sand body thickness, and dividing the fault-distance by the sand body thickness of each corresponding grid point to obtain the plane distribution gridding data of the sand fracture ratio.
Preferably, the formula for performing three-dimensional weighted stacking calculation on the multiple meshed and normalized dimensionless parameter data volumes in step 5 is as follows:
wherein: z' ij Representing the comprehensive evaluation score of a certain grid node j, n representing the number of parameter types participating in evaluation, Z ij A value, k, representing the jth mesh node of the ith dimensionless parametric data volume i Is the ith oneThe weighting coefficients of the dimension parameter data volume.
Compared with the background technology, the land-based hydrocarbon-containing basin favorable-to-reservoir-area multi-factor quantitative evaluation method has the following beneficial effects:
(1) The method can organically combine a plurality of geological parameters, save the huge workload of forming images one by one and manually superposing the individual parameters through the data processing capacity of a computer, has more scientific evaluation method, better accords with the actual geological condition of the prediction result, and improves the accuracy of the prediction of the favorable zone of the oil and gas reserves;
(2) Because the plane distribution of the parameters of the fault distance, the sand fracture ratio and the cap fracture ratio is calculated and relatively higher weight coefficients are given, the influence of faults can be considered more, and the method is more suitable for hydrocarbon-containing basins or strata series with stronger fracture activities such as fractured basins and the like.
Drawings
In order to better explain the evaluation method provided by the present invention and its advantages, the drawings used in the description of the embodiments are briefly introduced below so that those skilled in the art can further understand the present invention.
FIG. 1 is a schematic view of the structure of the present invention;
FIG. 2 is a diagram of a reservoir model for a region according to an embodiment of the present invention;
FIG. 3 is a plan view of a high quality source rock thickness for a region of an embodiment of the present invention;
FIG. 4 is a plan view of the source rock drainage intensity of a region according to an embodiment of the present invention;
FIG. 5 is a plan view of a small sand layer thickness in an area of an embodiment of the present invention;
FIG. 6 is a plan view of a small sand-to-ground ratio of a region in accordance with an embodiment of the present invention;
FIG. 7 is a plot of porosity of a small layer in a region of an embodiment of the present invention;
FIG. 8 is a plan view of a cap thickness for an area of an embodiment of the present invention;
FIG. 9 is a plan view of a vertical cross-sectional view of a small layer of a region in accordance with an embodiment of the present invention;
FIG. 10 is a plan view of a small sand break ratio for a region of an embodiment of the present invention;
FIG. 11 is a plan view of a small floor cap breaking ratio for a region of an embodiment of the present invention;
FIG. 12 is a plan view of a primary calculated vantage point of a region in accordance with an embodiment of the present invention;
FIG. 13 is a plan view of a region having its parameters adjusted for weight and then calculated for favorable regions in accordance with an embodiment of the present invention;
FIG. 14 is a schematic diagram of calculating horizontal pitch according to the present invention.
Detailed Description
Modifications and variations of this invention may occur to those skilled in the art based upon this disclosure.
A land-based hydrocarbon-containing basin favorable-to-reservoir-area multi-factor quantitative evaluation method comprises the following steps:
step 1, defining the main control factors of oil and gas reservoir: comprehensive analysis is carried out on six elements of 'living, storage, covering, enclosing, transportation and preservation' of the research area to form understanding of the reservoir formation rule of the research area and to determine the main control factors of the oil and gas reservoir formation;
step 2, screening main storage control parameters: determining main reservoir control parameters according to the clear oil and gas reservoir forming main control factors;
step 3, parameter gridding and normalization processing: determining main reservoir control parameters, collecting corresponding main reservoir control parameter data, gridding the determined main reservoir control parameter data to form a parameter data volume, and then carrying out dimensionless normalization to obtain a plurality of gridded and normalized three-dimensional non-dimensionless parameter data volumes;
wherein collecting the corresponding main reservoir control parameter data is as follows: the hydrocarbon source rock evaluation related parameters comprise thickness, oil drainage strength and the like; reservoir evaluation related parameters include thickness, porosity, sand-to-land ratio, etc.; the related parameters of the cover layer evaluation comprise thickness and the like; the trap effectiveness evaluation related parameters comprise a sand fracture ratio (vertical fault distance/sand layer thickness statistically calculated by a structural diagram) and the like; the oil gas storage conditions (the damage degree of the cover layer, the fault condition and the like) comprise a cover breaking ratio (vertical fault distance/cover layer thickness statistically calculated by a structural diagram) and the like;
the gridding processing process of the main reservoir control parameters comprises the following steps: calling a gridding function of software (a double fox geological mapping system or Surfer software and the like) with a three-dimensional data volume gridding function to perform planar gridding processing on the collected main reservoir control parameter data one by one to form a plurality of parameter data volumes;
the process of carrying out dimensionless normalization processing on the data after gridding each main reservoir control parameter comprises the following steps: according to the geological parameter characteristics of the research area, the gridded parameter data volume is scored, different evaluation coefficients are given to different parameters within different ranges, and if the evaluation coefficient range of a certain parameter is [ a, b ]]Given a corresponding parameter value range of [ Z ] 1 ,Z 2 ]When the parameter value is Z epsilon [ Z ∈ [ Z ] 1 ,Z 2 ]Then, the normalized parameter value Z' is:
the normalized parameter value is subjected to percentage system, namely the normalized parameter value is multiplied by 100, so that the evaluation significance is kept conveniently, and finally, a plurality of gridded and normalized three-dimensional dimensionless parameter data bodies are obtained.
The vertical fault-distance, sand-break ratio and cover-break ratio parameters collected in the step are used for calculating the vertical fault-distance, further calculating the sand-break ratio parameter and the cover-break ratio parameter, and a specific formula and an algorithm are used in the process of calculating the vertical fault-distance in a statistical manner.
The vertical fault distance statistical process comprises the following steps:
(1) A vector constructional diagram is needed, a fault plane distribution range (fault polygon data) and a constructional diagram depth or altitude contour line are extracted from the vector constructional diagram, and data points are encrypted according to certain precision requirements;
(2) For each point on the encrypted fault polygon, searching two corresponding nearest points of the other disc of the same fault, making a perpendicular line of the other two nearest points from the point, wherein the distance between the point and the foot is the horizontal fault distance, and the absolute value of the difference between the point and the foot in the depth or altitude value of the projection of the construction diagram is the vertical fault distance;
(3) And (4) performing the same calculation on each point on the fault polygon after plane encryption to obtain plane distribution data of the vertical fault distance.
The calculation process and method of the horizontal fault distance are as follows (as shown in fig. 14):
one point P outside the straight line 1 (x 1 ,y 1 ) Belongs to a certain point on a fault disc, and a straight line Ax + By + C =0 (A) 2 +B 2 Not equal to 0) is the distance P on the other disk of the slice 1 (x 1 ,y 1 ) Two points (P) with the closest point 2 (x 2 ,y 2 ),P 3 (x 3 ,y 3 ) ) line on, point P 1 (x 1 ,y 1 ) The distance formula to the above line is used to calculate the horizontal intercept d:
for a certain point on a fault disc, judging whether the corresponding point of another disc is judged by a vector method: the vector is directional, when a certain point is judged to be on the left side and the right side of the straight line, the left direction and the right direction are relative to the advancing direction, and the left direction and the right direction can be known as long as the advancing direction is specified, so that three points P on a plane are defined 1 (x 1 ,y 1 ),P 2 (x 2 ,y 2 ),P 3 (x 3 ,y 3 ) Three points form a triangle, the area is:
when P is present 1 ,P 2 ,P 3 S is positive in counterclockwise direction, P is positive 1 ,P 2 ,P 3 Clockwise S is negative, let the start of the vector be P 2 End point is P 3 And, the judged point is P 1 If S (P) 1 ,P 2 ,P 3 ) Is positive, then P 1 In the vector P 2 P 3 Left side of, if S (P) 1 ,P 2 ,P 3 ) Is negative, then P 1 In the vector P 2 P 3 Right side of if S (P) 1 ,P 2 ,P 3 ) Is 0, then P 1 On a straight line P 2 P 3 The above step (1); can judge the corresponding points on the two disks of the fault according to the data so as to avoidThe nearest two points are selected as points on the same fault disc.
The sand break ratio statistical process comprises the following steps:
and gridding the obtained plane distribution data of the vertical fault-distance and the plane distribution of the sand body thickness, and dividing the fault-distance by the sand body thickness of each corresponding grid point to obtain the plane distribution gridding data of the sand fracture ratio.
Step 4, endowing each dimensionless parameter data volume with a corresponding weighting coefficient: the found oil reservoir is used as calibration, and each three-dimensional dimensionless parameter data volume is given a certain weighting coefficient according to the importance and the analysis result of the reservoir forming main control factors;
the formula for calculating the favorable area by superposing a plurality of dimensionless parameter data volumes is as follows:
wherein: z' ij Representing the comprehensive evaluation score of a certain grid node j, n representing the number of parameter types participating in evaluation, Z ij A value, k, representing the jth mesh node of the ith dimensionless parametric data volume i The weighting coefficient of the ith dimensionless parameter data volume.
Step 6, verifying the evaluation result: judging whether the evaluation result meets the actual geological condition or not by using the known favorable ingredient region band, if not, readjusting the weight value or performing normalization again until the actual geological condition is met, and determining a final evaluation score;
and 7, obtaining a final evaluation conclusion: and judging the region with high evaluation score as a Tibetan favorable zone according to the percentile score.
Example 1
The application of the multi-factor quantitative evaluation method for the favorable-to-reserve area of the continental-facies hydrocarbon-containing basin is specifically described below by taking the Heilal basin Diwuerson-Bell depression N161 main oil reservoir as an example.
As shown in FIG. 1, the specific implementation process of the multi-factor quantitative evaluation method for the favorable reservoir area of the continental-phase hydrocarbon-containing basin of the invention comprises the following steps:
s1, selecting a related geological parameter map and data of a research area influencing oil and gas bearing property by taking six elements of 'birth, storage, covering, circling, transportation and protection' as a research main line according to geological multi-factor analysis of the research area;
each influence factor can be subdivided, and each main sub-factor is found out, so that the influence of some parameters due to inaccuracy or incomplete data on certain parameters is avoided;
s2, screening reservoir control geological factors, selecting parameters of main factors for controlling oil and gas reservoir formation according to reservoir formation mode characteristics of a research area, wherein the reservoir mode of the research area is shown in figure 2, and rejecting parameters which are seriously irrelevant and parameters which have little influence on oil and gas in the area;
the method comprises the following steps of determining a main oil reservoir type of a research area as a tectonic-lithologic oil reservoir through previous research cognition, considering that the fault influence accounts for a large proportion, a cover layer is generally developed and has a small influence, mainly considering a sand fracture ratio and a cover fracture ratio when considering fault parameter influence, determining evaluation coefficient assignment of each parameter normalization according to geological conditions of the research area, wherein the parameter normalization standard of the research area is shown in a table 1;
TABLE 1
According to the geological condition of a research area, the main parameters of the conditions of the source rocks are the thickness of the source rocks and the oil discharge strength;
the parameters selected by the reservoir condition are reservoir thickness, sand-to-ground ratio and porosity;
the selected parameter of the cover layer condition is the thickness of the cover layer;
the parameters selected for the trap effectiveness evaluation are the sand break ratio;
in the embodiment, the oil-gas migration condition of the fractured basin basically takes fault as a main factor, and other parameters are not used if the fracture-sand ratio parameter exists;
selecting a cover breaking ratio parameter by taking the damage degree of the cover layer as a storage condition;
and S3, sorting the multi-parameter data to obtain new parameters with geological significance. For the existing multiple geological parameter data, new and more effective parameters are obtained through the operation among the data. Parameters need to be redefined in the evaluation process to evaluate the characteristics of the related geological factors more accurately;
wherein, redefining new parameters in the embodiment includes: the sand-ground ratio, the sand breaking ratio and the cover breaking ratio are 3 parameters;
sand to ground ratio is the ratio of the evaluation zone sandstone thickness to the formation thickness;
the sand fracture ratio is the ratio of the vertical fault distance of the fault to the thickness of the sandstone;
the cap breaking ratio is the ratio of the vertical fault distance of the fault to the thickness of the cap layer;
s4, performing parameter dimensionless normalization, namely performing dimensionless normalization on each parameter data body derived in a gridding mode (the data of the high-quality hydrocarbon source rock thickness gridding three-dimensional data body part of the research area is shown in a table 2), and generating a dimensionless parameter data body (the data of the dimensionless parameter data body part of the high-quality hydrocarbon source rock gridding parameter data body of the research area after normalization is shown in a table 3);
TABLE 2
TABLE 3
| X | Y | After normalization |
| 514881.0813 | 5292070.5 | 77.584335 |
| 514974.9 | 5292070.5 | 77.65964106 |
| 515068.7188 | 5292070.5 | 77.73494712 |
| 515162.5375 | 5292070.5 | 77.81025317 |
| 514974.9 | 5292164.319 | 77.58520635 |
| 515068.7188 | 5292164.319 | 77.65289683 |
| 515162.5375 | 5292164.319 | 77.72058731 |
| 514974.9 | 5292258.138 | 77.51077163 |
| 515068.7188 | 5292258.138 | 77.57084654 |
| 515162.5375 | 5292258.138 | 77.63092144 |
Performing data statistics, gridding and normalization processing on all selected parameters in the embodiment according to the standard of the table 1;
s5, using the found oil reservoir as a calibration, carrying out weight assignment on reservoir control factors through the revealed result of well drilling data of a research area, analyzing the main reservoir control factors through dissecting the geological parameters of the existing oil reservoir, and giving the geological parameters with weights according to the importance of the reservoir control factors;
in the embodiment, the source rock and the fault have obvious control effect on the oil reservoir, and then the reservoir is followed by the trapping condition and the cap layer preservation condition (see fig. 3-11); wherein, the thickness plan view of the high-quality hydrocarbon source rock in the example area is shown in figure 3; the oil drainage intensity plan of the source rock in the area of the embodiment is shown in figure 4; the thickness plan of the small sand layer in the embodiment area is shown in figure 5; the plan view of the small sand ratio of the embodiment area is shown in figure 6; the plot of porosity of the small layer of the example area is shown in fig. 7; a plan view of the thickness of the cap layer in the area of the example is shown in FIG. 8; a small layer vertical fault-distance plan of the embodiment area is shown in figure 9; the sand breaking ratio plan view of the small layer of the embodiment area is shown in figure 10; the plan view of the small layer of the broken cover ratio of the embodiment area is shown in fig. 11.
S6, carrying out multi-parameter fusion, calculating score values of the evaluation zones, carrying out weighting combination calculation on a plurality of dimensionless parameter data bodies, and determining a favorable accumulation zone;
preliminarily setting weight coefficients (the thickness of the hydrocarbon source rock is 0.1, the oil drainage strength is 0.1, the thickness of a reservoir is 0.2, the sand-land ratio is 0.1, the porosity of the reservoir is 0.2, the thickness of a cover layer is 0.1, the sand fracture ratio is 0.1 and the cover fracture ratio is 0.1), obtaining initial evaluation calculation results, and obtaining a primary calculation favorable area plan of a research area as shown in FIG. 12;
s7, observing whether the evaluation result meets the actual geological condition of the oiliness of the zone revealed by the known well drilling;
wherein, the initial evaluation result has larger error with the actual drilling result (figure 12);
s8, readjusting the weight value until the actual geological condition is met;
wherein, the weight in the embodiment is adjusted, and the following evaluation calculation is carried out again;
s6, multi-parameter fusion, namely calculating score of the evaluation zone, and performing weighted combination calculation on the multi-item dimensionless parameter data bodies to determine a favorable accumulation zone;
after the weight coefficients are adjusted for multiple times, a set of weight coefficients (the thickness of the source rock is 0.1, the oil drainage strength is 0.15, the thickness of a reservoir is 0.1, the sand-to-ground ratio is 0.15, the porosity of the reservoir is 0.15, the thickness of a cover layer is 0.1, the sand fracture ratio is 0.2, and the cover fracture ratio is 0.05) are finally determined, so that a final evaluation calculation result is obtained, and a favorable area plan view is calculated after the weight of parameters is adjusted and is shown in fig. 13;
s7, observing whether the evaluation result meets the actual geological condition of the oiliness of the zone disclosed by the known well drilling;
the matching degree of the final evaluation result and the actual drilling result is high, and the prediction effect is good;
and S9, outputting a calculation result, and judging the sequence of the Tibetan favorable zones according to the percentile values.
According to the technical scheme, scientific and relatively reliable prediction can be carried out on the favorable collection area of the basin, the prediction result is closely related to the research data of an actual research area, the areas can only be qualitatively analyzed by manually drawing independent parameters and overlapping the areas in the conventional evaluation process, the prediction accuracy is low, the parameter weight can be modified in real time through the powerful calculation capability of a computer, the calculation can be carried out for multiple times in a short period, different geological parameters can be selected according to the geological conditions of different research areas, and a large amount of time for repeated work of geological researchers is saved.
Claims (9)
1. A land-phase hydrocarbon-containing basin favorable-reservoir-area multi-factor quantitative evaluation method is characterized by comprising the following steps:
step 1, defining the main control factors of oil and gas reservoir: comprehensive analysis is carried out on six elements of 'generation, storage, covering, enclosing, transportation and preservation' of a research area to form understanding of the storage rule of the research area and to clarify the main control factors of oil and gas storage;
step 2, screening main storage control parameters: determining main reservoir control parameters according to the clear oil and gas reservoir formation main control factors;
step 3, parameter gridding and normalization processing: determining main reservoir control parameters, collecting corresponding main reservoir control parameter data, gridding the determined main reservoir control parameter data to form a parameter data volume, and then carrying out dimensionless normalization to obtain a plurality of gridded and normalized three-dimensional non-dimensionless parameter data volumes;
step 4, endowing each dimensionless parameter data volume with a corresponding weighting coefficient: the found oil reservoir is used as calibration, and each three-dimensional dimensionless parameter data volume is given a certain weighting coefficient according to the importance and the analysis result of the reservoir forming main control factors;
step 5, overlapping and calculating the beneficial areas of the plurality of dimensionless parameter data volumes: multiplying the three-dimensional dimensionless parameter data volume by a weighting coefficient, and then carrying out superposition calculation to obtain the evaluation score of each zone, wherein a zone favorable to the accumulation is obtained when the evaluation score is high;
step 6, verifying the evaluation result: judging whether the evaluation result meets the actual geological condition or not by using the known favorable reservoir zone, if not, readjusting the weight value or performing normalization again until the actual geological condition is met, and determining a final evaluation score;
and 7, obtaining a final evaluation conclusion: and judging the region with high evaluation score as a Tibetan favorable zone according to the percentile score.
2. The method for multi-factor quantitative evaluation of favorable reservoir zone of terrestrial hydrocarbon-bearing basin according to claim 1, wherein the method comprises the following steps: the step 3 of collecting the corresponding main storage control parameter data comprises the following steps: the evaluation related parameters of the hydrocarbon source rock comprise thickness and oil drainage strength; reservoir evaluation related parameters include thickness, porosity, sand-to-ground ratio; the cap layer evaluation-related parameters include thickness; the trap effectiveness evaluation related parameters comprise a sand break ratio; the oil gas conservation conditions comprise a cover breaking ratio.
3. The method for quantitatively evaluating the land-based hydrocarbon-bearing basin favorable burial zone multi-factor according to claim 1, wherein the method comprises the following steps: the parameter gridding processing process in the step 3 is as follows: and calling the gridding function of the software with the three-dimensional data volume gridding function to perform plane gridding processing on the collected main storage control parameter data one by one to form a plurality of parameter data volumes.
4. The method for quantitatively evaluating the land-based hydrocarbon-bearing basin favorable burial zone multi-factor according to claim 1, wherein the method comprises the following steps: the process of performing dimensionless normalization on the parameter data volume after gridding each main reservoir control parameter in the step 3 is as follows: according to the geological parameter characteristics of the research area, the parameter data volume after gridding is scored, different evaluation coefficients are given to different parameters within different ranges, if the evaluation coefficient range of a certain parameter is [ a, b ]]Given a corresponding parameter value range of [ Z ] 1 ,Z 2 ]When the parameter value is Z epsilon [ Z ∈ [ Z ] 1 ,Z 2 ]Then, the normalized parameter value Z' is:
the normalized parameter values are subjected to percentage system, namely the normalized parameter values are multiplied by 100, so that the evaluation significance is kept conveniently, and a plurality of gridded and normalized three-dimensional parameter data bodies are finally obtained.
5. The method of claim 1, wherein the formula for calculating the profitable area by superposition of the plurality of dimensionless parameter data volumes in step 5 is
Wherein: z' ij Representing the comprehensive evaluation score of a certain grid node j, n representing the number of parameter types participating in evaluation, Z ij A value, k, representing the jth mesh node of the ith dimensionless parametric data volume i The weighting coefficient of the ith dimensionless parameter data volume.
6. The method for multi-factor quantitative evaluation of favorable reservoir areas of land-based hydrocarbon-bearing basins and reservoir areas according to claim 2, wherein vertical fault distances are applied to the calculation of the sand-breaking ratio parameters and the cover-breaking ratio parameters, and the calculation of the vertical fault distances needs to utilize collected construction diagrams; a specific formula and algorithm are used in the process of calculating the vertical fault distance in a statistical manner; the sand break ratio is the ratio of the vertical fault distance to the sand layer thickness calculated by the statistics of a construction diagram; the cap break ratio is the ratio of the vertical distance to the cap layer thickness statistically calculated from the formation map.
7. The method for multi-factor quantitative evaluation of favorable reservoir zone of terrestrial hydrocarbon-bearing basin according to claim 6, wherein the statistical process of vertical fault distance is as follows:
(1) Extracting a fault plane distribution range (fault polygon data) and a structural map depth or altitude contour line from the vector structural map, and encrypting data points according to certain precision requirements;
(2) For each point on the encrypted fault polygon, searching two corresponding nearest points of the other disc of the same fault, making a perpendicular line of the other two nearest points from the point, wherein the distance between the point and the foot is the horizontal fault distance, and the absolute value of the difference between the depth or the altitude value of the projection of the point and the foot on the construction diagram is the vertical fault distance;
(3) And (4) performing the same calculation on each point on the fault polygon after plane encryption to obtain plane distribution data of the vertical fault distance.
8. The method for multi-factor quantitative evaluation of favorable reservoir zones of terrestrial hydrocarbon-bearing basins according to claim 7, wherein the horizontal fault distance is calculated by the following steps:
one point P outside the straight line 1 (x 1 ，y 1 ) Belongs to a certain point on a fault disc, and a straight line Ax + By + C =0 (A) 2 +B 2 Not equal to 0) is the distance P on the other disk of the slice 1 (x 1 ，y 1 ) Two points (P) with the closest points 2 (x 2 ，y 2 )，P 3 (x 3 ，y 3 ) ) line on, point P 1 (x 1 ，y 1 ) The distance formula to the above line is used to calculate the horizontal intercept d:
for a certain point on a fault disc, judging whether the corresponding point of another disc is judged by a vector method: the vector is directional, when a certain point is judged to be on the left side and the right side of the straight line, the left direction and the right direction are relative to the advancing direction, and the left direction and the right direction can be known as long as the advancing direction is specified, and three points P on a plane are defined 1 (x 1 ，y 1 )，P 2 (x 2 ，y 2 )，P 3 (x 3 ，y 3 ) Three points form a triangle, the area is:
when P is 1 ，P 2 ，P 3 S is positive in counterclockwise direction, P is positive 1 ，P 2 ，P 3 Clockwise S is negative, let the starting point of the vector be P 2 End point is P 3 And, the judged point is P 1 If S (P) 1 ，P 2 ，P 3 ) Is a positive number, then P 1 In the vector P 2 P 3 Left side of, if S (P) 1 ，P 2 ，P 3 ) Is negative, then P 1 In the vector P 2 P 3 Right side of if S (P) 1 ，P 2 ，P 3 ) Is 0, then P 1 On a straight line P 2 P 3 The above step (1); corresponding points on two disks of the fault can be judged according to the data so as to avoid selecting the nearest two points as the points on the same disk of the fault.
9. The method for multi-factor quantitative evaluation of favorable reservoir zone of land-based hydrocarbon-bearing basin according to claim 3 or 7, wherein the statistical process of sand fracture ratio is as follows: and (3) respectively gridding the plane distribution data of the vertical fault-distance obtained in the step (7) and the plane distribution data of the sand body thickness obtained in the step (3), and dividing the fault-distance by the sand body thickness of each corresponding grid point to obtain the plane distribution gridding data of the sand fracture ratio.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110842222.8A CN115685371A (en) | 2021-07-26 | 2021-07-26 | Multi-factor quantitative evaluation method for favorable-to-reserve area of continental-facies hydrocarbon-containing basin |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110842222.8A CN115685371A (en) | 2021-07-26 | 2021-07-26 | Multi-factor quantitative evaluation method for favorable-to-reserve area of continental-facies hydrocarbon-containing basin |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115685371A true CN115685371A (en) | 2023-02-03 |
Family
ID=85044940
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110842222.8A Pending CN115685371A (en) | 2021-07-26 | 2021-07-26 | Multi-factor quantitative evaluation method for favorable-to-reserve area of continental-facies hydrocarbon-containing basin |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115685371A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116402880A (en) * | 2023-06-06 | 2023-07-07 | 昆仑数智科技有限责任公司 | Method, device, equipment and storage medium for determining oil-containing area |
-
2021
- 2021-07-26 CN CN202110842222.8A patent/CN115685371A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116402880A (en) * | 2023-06-06 | 2023-07-07 | 昆仑数智科技有限责任公司 | Method, device, equipment and storage medium for determining oil-containing area |
| CN116402880B (en) * | 2023-06-06 | 2024-01-19 | 昆仑数智科技有限责任公司 | Method, device, equipment and storage medium for determining oil-containing area |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111582386A (en) | Random forest based geological disaster multi-disaster comprehensive risk evaluation method | |
| CN114971301B (en) | Ecological interference risk identification and evaluation method based on automatic parameter adjustment optimization model | |
| CN104747185A (en) | Heterogeneous reservoir stratum synthetic classifying evaluation method | |
| CN103714390A (en) | Oil and gas trap combination exploration optimization method | |
| Mozaffari et al. | Estimating particle-size distribution from limited soil texture data: Introducing two new methods | |
| CN109118004B (en) | Prediction method for suitable area for engineering construction site selection | |
| CN115685371A (en) | Multi-factor quantitative evaluation method for favorable-to-reserve area of continental-facies hydrocarbon-containing basin | |
| CN114818076A (en) | Machine learning-based fault closed hydrocarbon column height evaluation method | |
| CN106526675A (en) | Automatic extraction method for fault space parameter | |
| Yang | Evaluation of spatial variation of water resources carrying capacity using optimal method: a case study of Fujian, China | |
| CN114239962A (en) | Refined space load prediction method based on open source information | |
| JP2000002769A (en) | Method and device for predicting the spatial distribution of geological structure to prepare geological map | |
| Choobbasti et al. | Mapping of soil layers using artificial neural network (case study of Babol, northern Iran) | |
| CN110956388B (en) | Method for generating yield increasing scheme of offshore oil and gas reservoir | |
| CN116934125A (en) | Ecological bearing capacity evaluation method for energy development area | |
| Yang et al. | Intelligent rating method of tunnel surrounding rock based on one-dimensional convolutional neural network | |
| CN115906669A (en) | Dense residual error network landslide susceptibility evaluation method considering negative sample selection strategy | |
| CN107103552A (en) | Exploration efficiency coefficient determines method and device | |
| Khoshoui et al. | Analyzing the homogeneity of the paired catchments using the fractal dimension of the drainage network and catchment shape | |
| Shiyun et al. | A fine assessment technology for medium-high exploration degree plays | |
| CN109165465B (en) | Highway landslide damage loss evaluation model and construction method thereof | |
| CN111929744B (en) | Kendall's coefficient-based multivariate information reservoir classification method | |
| CN116542808A (en) | Mine environment bearing capacity determining method, system, electronic equipment and medium | |
| CN115263282B (en) | Deep compact sandstone reservoir type identification method and electronic equipment | |
| CN106227959A (en) | Method and device for predicting lithologic reservoir favorable area based on four-graph superposition method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |