CN-121998146-A - Dual-model tight gas sandstone reservoir productivity prediction method based on logging technology

Abstract

The invention discloses a dual-model tight gas sandstone reservoir capacity prediction method based on a logging technology, which comprises the steps of S1, determining gas measurement parameters related to reservoir gas content, S2, determining a tight gas reservoir shaft information capacity prediction model, S2, specifically, selecting data, S22, introducing new parameters, S23, establishing a multi-layer gas testing capacity weight distribution model, adopting a capacity distribution model for a multi-layer gas testing well as a single-layer AOF Decomposition of ＝AOF×S Layer(s) /∑S Total (S) , S24, establishing a dual-model tight gas sandstone reservoir capacity prediction model, and adopting a capacity prediction model established by single-layer gas testing well data as AOF 1 ＝0.0861×(TG Product of /V TG /T TG ×J TG ) 0.6262 ,R 2 = 0.7543, and establishing a capacity prediction model established by multi-layer gas testing well data as AOF 2 ＝0.2096×(TG Product of /V TG /T TG ×J TG ) 0.8473 ,R 2 = 0.7542. The method can realize the prediction of the productivity of the vertical and inclined wells of the tight gas sandstone reservoir, has high prediction precision and low cost, is suitable for the oil and gas exploitation industry, and is used for evaluating the productivity of unconventional oil and gas reservoirs.

Inventors

• ZHAO HUIXIA
• HE QIANG
• WANG GANG
• ZHANG GUOBING
• WANG JUN
• WEN ZHU
• ZHAO TIANDONG
• WANG LIYONG
• ZHANG JIE
• YANG HAN

Assignees

• 中国石油集团渤海钻探工程有限公司
• 中国石油天然气集团有限公司

Dates

Publication Date

20260508

Application Date

20241106

Claims (3)

1. 1. A method for predicting the production capacity of a dual-model tight gas sandstone reservoir based on a logging technology is characterized by comprising the following steps: s1, determining gas measurement parameters related to the gas content of a reservoir, wherein the gas measurement parameters comprise a full hydrocarbon value TG, a hydrocarbon slope Gr, a humidity ratio WH and an equilibrium ratio BH; s2, determining a tight gas reservoir well bore information productivity prediction model, wherein the method comprises the steps of, S21, selecting data, sorting and classifying according to a classification principle of a relative single layer and an independent multi-layer on the basis of a gas testing layer section; S22, introducing new parameters, processing the gas-measurement total hydrocarbon value TG by using a statistical method, introducing an integral and standard deviation processing method to form new productivity prediction evaluation parameters, and optimizing the reservoir energy and the heterogeneity of the reservoir by introducing the new parameters so as to accurately reflect formation energy information by the total hydrocarbon value parameters: s23, establishing a multi-layer test gas productivity weight distribution model; The productivity distribution model adopted by the multi-layer gas testing well is a single-layer AOF Decomposition of ＝AOF×S Layer(s) /∑S Total (S) , wherein the AOF is an unobstructed flow, S Layer(s) is a single-layer total hydrocarbon value integral area, and S Total (S) is the total hydrocarbon value integral area of the gas testing layer; s24, establishing a dual-model tight gas sandstone reservoir productivity prediction model; the productivity prediction model established by single-layer gas test well data is that the predicted productivity AOF 1 ＝0.0861×(TG Product of /V TG /T TG ×J TG ) 0.6262 ,R 2 = 0.7543; the capacity prediction model established by the multi-layer gas test well data is the predicted capacity AOF 2 ＝0.2096×(TG Product of /V TG /T TG ×J TG ) 0.8473 ,R 2 = 0.7542.
2. 2. The logging technique-based dual-model tight gas sandstone reservoir production prediction method of claim 1, wherein the full hydrocarbon value parameters in step S22 comprise a full hydrocarbon integration area TG Product of , a full hydrocarbon variation coefficient V TG , a full hydrocarbon mutation coefficient T TG and a full hydrocarbon range J TG , wherein TG Product of is the full hydrocarbon value integration area of the gas logging, V TG is the ratio of the standard deviation of the full hydrocarbon to the average of the full hydrocarbon, T TG is the ratio of the maximum value of the full hydrocarbon to the average of the full hydrocarbon, and J TG is the ratio of the maximum value of the full hydrocarbon to the minimum value of the full hydrocarbon.
3. 3. The logging technique-based dual-model tight gas sandstone reservoir production prediction method of claim 1 or 2, wherein step S2 further comprises, S25, modifying the applicability of the dual-model compact gas sandstone reservoir productivity prediction model; the correction is carried out on the productivity prediction model obtained in the step S24 by introducing a correction coefficient K for the data acquired by adopting the non-quantitative degasser: predicted capacity AOF 1 ＝K×0.0861×(TG Product of /V TG /T TG ×J TG ) 0.6262 ,R 2 = 0.7543; predicted capacity AOF 2 ＝K×0.2096×(TG Product of /V TG /T TG ×J TG ) 0.8473 ,R 2 = 0.7542; Wherein, when a quantitative degasser is used, K=1, when a non-quantitative degasser is used, K=3 when the whole hydrocarbon value is less than 60%, and K=6 when the whole hydrocarbon value is more than 60%.

Description

Dual-model tight gas sandstone reservoir productivity prediction method based on logging technology Technical Field The invention belongs to the field of unconventional tight oil and gas exploration, and particularly relates to a dual-model tight gas sandstone reservoir productivity prediction method based on a logging technology. Background The productivity evaluation is a technology for comprehensively evaluating the oil production capacity of a reservoir, and has great significance for exploration and development of oil and gas fields. The productivity evaluation can not only improve the exploration and development benefits, but also provide important scientific basis for deployment and planning of development schemes. Dense gas is a low abundance, low pore, low permeability natural gas resource stored in dense rock underground. Dense gas of the Erdos basin has the characteristics of deeper burial, worse reservoir, irregular distribution and the like. For medium and high permeability reservoirs, the oil-water seepage basically obeys Darcy seepage law, and the productivity evaluation is relatively simple. However, the low-speed seepage state in the low-permeability oil reservoir does not conform to darcy law, and the reason is that besides viscous resistance, the low-speed seepage state is also subjected to adsorption resistance of fluid and rock or attraction resistance of a water film, and only when the resistance is overcome, the liquid can flow. Thus, the capacity assessment of such low pore, low permeation or ultra low permeation layers with initiation pressure phenomena is difficult and heavy. At present, most reliable data for reservoir productivity evaluation is provided by oil testing and production testing data, and the early cost of the method is high. Disclosure of Invention The invention aims to provide a dual-model tight gas sandstone reservoir productivity prediction method based on a logging technology, which aims to solve the problem of high early-stage cost caused by the utilization of oil testing and production testing data to provide reservoir productivity evaluation in the prior art. In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: a method for predicting capacity of a dual-model tight gas sandstone reservoir based on logging technology comprises the following steps: s1, determining gas measurement parameters related to the gas content of a reservoir, wherein the gas measurement parameters comprise a full hydrocarbon value TG, a hydrocarbon slope Gr, a humidity ratio WH and an equilibrium ratio BH; s2, determining a tight gas reservoir well bore information productivity prediction model, wherein the method comprises the steps of, S21, selecting data, sorting and classifying according to a classification principle of a relative single layer and an independent multi-layer on the basis of a gas testing layer section; S22, introducing new parameters, processing the gas-measurement total hydrocarbon value TG by using a statistical method, introducing an integral and standard deviation processing method to form new productivity prediction evaluation parameters, and optimizing the reservoir energy and the heterogeneity of the reservoir by introducing the new parameters so as to accurately reflect formation energy information by the total hydrocarbon value parameters: s23, establishing a multi-layer test gas productivity weight distribution model; The productivity distribution model adopted by the multi-layer gas testing well is a single-layer AOF Decomposition of ＝AOF×S Layer(s) /∑S Total (S) , wherein the AOF is an unobstructed flow, S Layer(s) is a single-layer total hydrocarbon value integral area, and S Total (S) is the total hydrocarbon value integral area of the gas testing layer; s24, establishing a dual-model tight gas sandstone reservoir productivity prediction model; the productivity prediction model established by single-layer gas test well data is that the predicted productivity AOF 1＝0.0861×(TG Product of /VTG/TTG×JTG)0.6262,R2 = 0.7543; the capacity prediction model established by the multi-layer gas test well data is the predicted capacity AOF 2＝0.2096×(TG Product of /VTG/TTG×JTG)0.8473,R2 = 0.7542. The invention is limited by the parameters of the whole hydrocarbon value in the step S22, including a whole hydrocarbon integral area TG Product of , a whole hydrocarbon variation coefficient V TG, a whole hydrocarbon mutation coefficient T TG and a whole hydrocarbon range J TG, wherein TG Product of is the whole hydrocarbon value integral area of the gas logging, V TG is the ratio of the standard deviation of the whole hydrocarbon to the average value of the whole hydrocarbon, T TG is the ratio of the maximum value of the whole hydrocarbon to the average value of the whole hydrocarbon, and J TG is the ratio of the maximum value of the whole hydrocarbon to the minimum value of the whole hydrocarbon. As another limitation o