CN-121995516-A - Reservoir productivity prediction method and electronic equipment

CN121995516ACN 121995516 ACN121995516 ACN 121995516ACN-121995516-A

Abstract

The invention provides a reservoir productivity prediction method and electronic equipment, which belong to the technical field of petroleum geophysical exploration, wherein the method comprises the steps of calculating reservoir physical parameters based on natural gamma logging data and acoustic time difference logging data; the method comprises the steps of obtaining the high-conductivity fracture parameters of a reservoir, calculating the high-conductivity fracture parameters of the reservoir based on the electric imaging logging data, calculating the far-end fracture parameters beside the well based on the acoustic wave far-detection logging data, constructing the comprehensive productivity logging index based on the physical property parameters of the reservoir, the high-conductivity fracture parameters and the far-end fracture parameters, achieving the purpose of improving the comprehensive prediction precision of the productivity of the reservoir, and providing a basis for the gas testing, the layer selection, the development and the utilization of the reservoir.

Inventors

WEI ZHIHONG
HAN JIYONG
ZENG TAO
WANG KUN
WANG JIANBO
CHEN DAN
HUANG QIUJING
WU LEI

Assignees

中国石油化工股份有限公司
中国石油化工股份有限公司勘探分公司

Dates

Publication Date: 20260508
Application Date: 20241101

Claims (10)

1. A method of reservoir production prediction, comprising: Calculating physical parameters of the reservoir based on natural gamma logging data and acoustic time difference logging data; calculating high-conductivity fracture parameters of the reservoir based on the electric imaging logging data; Calculating a near-well far-end fracture parameter based on acoustic far-detection logging information; and constructing a comprehensive productivity logging index based on the reservoir physical property parameter, the high-conductivity fracture parameter and the remote fracture parameter.
2. The method of reservoir capacity prediction as recited in claim 1, wherein the reservoir physical parameters include effective reservoir porosity and effective reservoir thickness.
3. The method of reservoir capacity prediction as set forth in claim 2, wherein, The calculation of reservoir physical parameters based on natural gamma and acoustic time difference logging data comprises the following steps: calibrating logging data by using core analysis and test data; Calculating the effective reservoir porosity according to a porosity calculation method; and calculating the sum of stratum thicknesses of which the effective reservoir porosity is larger than the lower limit value of the effective reservoir porosity to obtain the effective reservoir thickness.
4. The method of reservoir capacity prediction as set forth in claim 3, wherein, The effective reservoir porosity is calculated by the following formula: Wherein phi T is the acoustic porosity, delta t ma is the acoustic time difference of the rock skeleton, delta t f is the acoustic time difference of the formation fluid, delta t sh is the acoustic time difference of the mudstone, delta t is the acoustic time difference of the target layer, C p is the acoustic compaction correction coefficient, and V sh is the mudmass content; The formula of V sh is: Where SH is the relative value of natural gamma, GR is the measured value of natural gamma, GR min is the minimum value of natural gamma, GR max is the maximum value of natural gamma, and GC is the empirical coefficient.
5. The method of claim 1, wherein the high conductivity fracture parameters include the number of high conductivity fractures in the reservoir, the length of the high conductivity fractures in the reservoir, and the width of the high conductivity fractures in the reservoir.
6. The method of reservoir capacity prediction as recited in claim 5, wherein, The calculation of the reservoir high-conductivity fracture parameters based on the electrical imaging logging data comprises the following steps: Counting the sum of the number of the picked cracks in the set window length to obtain the number of the high-conductivity cracks of the reservoir; counting the sum of the lengths of the picked cracks in the set window length to obtain the length of the high-conductivity crack of the reservoir; the reservoir high conductivity fracture width is calculated by the following formula: Wherein W is the width of a high-conductivity fracture of the reservoir, A and B are empirical values, C is the abnormal area of conductivity caused by the fracture, R xo is the rock skeleton resistivity of the fracture, and R m is the fluid resistivity in the fracture.
7. The method of reservoir capacity prediction as recited in claim 1, wherein the near-wellbore far-end fracture parameters include a near-wellbore far-end fracture number and a near-wellbore far-end fracture length.
8. The method of reservoir capacity prediction as claimed in claim 7, wherein, The method for calculating the parameters of the remote crack beside the well based on the acoustic remote detection logging data comprises the following steps: And identifying the geological reflector outside the well based on acoustic wave remote detection logging data, judging the development condition of the far-end cracks beside the well, and calculating to obtain the number of the far-end cracks beside the well and the length of the far-end cracks beside the well.
9. The method of reservoir capacity prediction as set forth in claim 1, wherein, Calculating the comprehensive productivity logging index by the following formula: Where Q logging is the integrated capacity logging index, H is the effective reservoir thickness, POR is the average effective porosity within the effective reservoir thickness, AVEFMIFRAC (W) is the reservoir high conductivity fracture width, EFMIfrac (w) is the reservoir high conductivity fracture length, ARIfrac (w) is the well side distal fracture length, m is the reservoir high conductivity fracture number, n is the well side distal fracture number, and a, b, c and d are regional empirical parameters.
10. An electronic device, the electronic device comprising: At least one processor, and A memory communicatively coupled to the at least one processor, wherein, The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the reservoir capacity prediction method of any one of claims 1-9.

Description

Reservoir productivity prediction method and electronic equipment Technical Field The invention belongs to the technical field of petroleum geophysical exploration, and particularly relates to a reservoir productivity prediction method and electronic equipment. Background The fracture body has poor physical properties, extremely strong heterogeneity, complex crack development condition, difficult evaluation of reservoir effectiveness and large test productivity difference, and brings great disadvantages to the work of gas test research, reservoir development and utilization, and the like. At present, the method for predicting the productivity of the reservoir by using logging data at home and abroad mainly analyzes the effectiveness of the reservoir by integrating factors such as lithology, physical properties and the like, thereby achieving the purpose of productivity prediction. For example, the porosity, permeability and saturation are utilized to estimate reservoir production capacity in the first prior art, the residual wells of a research area 300 are comprehensively analyzed, single factor analysis of lithology, reservoir property, permeability, fracture and cave development degree and reservoir effectiveness is finally integrated, comprehensive quality RQ representing the reservoir is constructed, the comprehensive quality coefficient RPI of the reservoir is calculated by combining the thicknesses of different reservoir classifications, a relation is established between the comprehensive quality coefficient RPI and test productivity, a correlation analysis method and a sensitive parameter analysis method are adopted in the third prior art, a matrix comprehensive index f1 and a fracture effectiveness index f2 are constructed, a productivity prediction bubble plate based on f1 and f2 indexes is established, and the compliance of the prediction plate reaches 93.3% through oil testing result verification. The prior art is mainly used for evaluating physical properties of a reservoir matrix and wall cracks, so that the reservoir productivity is predicted, and a broken seam body reservoir for controlling the productivity by taking the cracks as main control factors is not considered, so that the influence of the far-end cracks beside the well on the reservoir productivity is not considered, and the method has certain limitation. In addition, the yield of the reservoir is controlled by the dual control of pores and cracks, and the reservoir with the fracture body has strong heterogeneity and complex lithology, so that the calculation of physical parameters such as the porosity of the reservoir is difficult to achieve, and meanwhile, the evaluation of the effectiveness of the cracks and the extension condition of the cracks is difficult to achieve, so that the research on the gas-testing and layer-selecting is difficult, and the reservoir with the fracture body has large test productivity difference. Disclosure of Invention The invention aims to provide a reservoir productivity prediction method and electronic equipment, which solve the problem of low comprehensive reservoir productivity prediction precision in the prior art. To achieve the above object, in a first aspect, the present invention provides a reservoir productivity prediction method, including: Calculating physical parameters of the reservoir based on natural gamma logging data and acoustic time difference logging data; calculating high-conductivity fracture parameters of the reservoir based on the electric imaging logging data; Calculating a near-well far-end fracture parameter based on acoustic far-detection logging information; and constructing a comprehensive productivity logging index based on the reservoir physical property parameter, the high-conductivity fracture parameter and the remote fracture parameter. Further, the reservoir physical parameters include effective reservoir porosity and effective reservoir thickness. Further, the calculating reservoir physical parameters based on the natural gamma and acoustic time difference logging data comprises: calibrating logging data by using core analysis and test data; Calculating the effective reservoir porosity according to a porosity calculation method; and calculating the sum of stratum thicknesses of which the effective reservoir porosity is larger than the lower limit value of the effective reservoir porosity to obtain the effective reservoir thickness. Further, the effective reservoir porosity is calculated by the following formula: Wherein phi T is the acoustic porosity, delta t ma is the acoustic time difference of the rock skeleton, delta t f is the acoustic time difference of the formation fluid, delta t sh is the acoustic time difference of the mudstone, delta t is the acoustic time difference of the target layer, C p is the acoustic compaction correction coefficient, and V sh is the mudmass content; The formula of V sh is: Where SH is the relative value of natural gamma, GR is t