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A method for quantitative prediction of gas-bearing probability by pre-stack afi inversion

A probabilistic and inversion technology, applied in the field of seismic exploration data reservoir prediction, can solve uncertainties and other problems, achieve the effect of improving accuracy and improving drilling success rate

Active Publication Date: 2011-12-21
BC P INC CHINA NAT PETROLEUM CORP +2
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Problems solved by technology

[0006] The purpose of the present invention is to overcome the above-mentioned problems existing in the existing oil and gas detection technology, and to provide a method for quantitatively predicting the gas-bearing probability by pre-stack AFI inversion. The present invention solves the uncertainty problem of AVO in oil and gas detection. In the analysis, the uncertainty is transformed into a deterministic analysis method of gas-containing probability distribution, and a more accurate gas-containing probability distribution is obtained through quantitative inversion of gas-containing probability

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  • A method for quantitative prediction of gas-bearing probability by pre-stack afi inversion

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Embodiment 1

[0032] A method for quantitatively predicting gas-bearing probability by pre-stack AFI inversion, comprising the following steps:

[0033] a. Use Vp, Vs, Density and other logging data to conduct trend analysis. On the basis of trend surface analysis, according to the depth range of the reservoir, randomize the probability distribution of the layer-surface characteristic parameters of n depths in the target interval Simulation, random geological models at different depths can be obtained by random combination of different parameters. The trend surface results of sandstone and mudstone velocity and density changing with depth determined during stochastic modeling come from trend analysis of logging data.

[0034] b. Use the Biot-Gassman method to perform fluid replacement for each random geological model, and obtain the corresponding responses of the sandstone models in various fluid combination states, and form three different depth template maps on the intercept I-gradient G ...

Embodiment 2

[0037] A method for quantitatively predicting gas-bearing probability by pre-stack AFI inversion, comprising the following steps:

[0038] a. In the Weidong 3D block, use the logging data of 4 wells for trend analysis. The premise of trend surface analysis is to assume that the analyzed rock parameters are normal Gaussian distribution, and use the standard deviation and average value of the curve to determine the probability distribution of formation characteristic parameters at different depths. The trend surface results of the velocity and density of sandstone and mudstone determined by stochastic modeling based on probability as a function of depth. From the trend diagram of this area, we can see that the sandstone velocity gradually increases with depth, but between 2000 and 2200m, that is, the sandstone velocity and density near the target layer tend to decrease, and the sandstone porosity increases with depth. The change trend of mudstone is basically consistent with th...

Embodiment 3

[0046] According to the prediction results, 5 suggested well locations were put forward in the study area. After drilling, Yue 001-x12 produced 92×10 gas in the second member of the Xu 4 m 3 / d, daily oil production 102m 3 It is the well with the best production capacity in this area. Well Weidong 12 produced 8.15×10 gas in Xu 2 Member 4 m 3 / d, daily oil production 9.2m 3 , and achieved huge economic benefits, with an estimated benefit of 3 billion yuan. The success of drilling with this result has greatly increased the reserves of the Xujiahe Formation in this area. The drilling success rate is greatly improved.

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Abstract

The invention discloses a method for quantificationally predicting gas containing probability through prestack automatic fault indication (AFI) inversion, which comprises the following steps of: using log information for carrying out trend analysis, carrying out random simulation on the probability distribution condition of n depth layer characterization parameters of a target layer section, and obtaining the random geology model in different depth positions through the random combination of different parameters; carrying out fluid replacement on each random geology model, obtaining the corresponding response of sandstone models in various fluid states and forming three different depth template patterns; and correcting amplitude versus offset (AVO) attribute points of the actual chemical mechanical polishing (CMP) gather data body, projecting the corrected actual data point results to the corresponding depth template patterns and obtaining the quantificationally predicted probability distribution of the gas containing probability. The method of the invention solves the problem of uncertainty of the AVO existing in the oil gas detection and belongs to a method of converting the uncertainty in the AVO analysis into the certainty of the gas containing probability distribution, and the accurate gas containing probability distribution is obtained through the quantitative inversion of the gas containing probability.

Description

technical field [0001] The invention relates to a method for quantitatively predicting gas-bearing probability by pre-stack AFI inversion, and belongs to the field of seismic exploration data reservoir prediction. Background technique [0002] AVO (Amplitude versus Offset) studies the relationship between seismic longitudinal wave amplitude and offset. Its theoretical basis is the Zoeppritz equation describing the energy relationship between various reflected waves and transmitted waves generated by plane longitudinal waves at the impedance interface. [0003] Because the Zoeppritz equation is too complex, it is difficult to directly see the parameters that have a direct impact on the reflection coefficient. Over the years, many scholars have derived its approximate expression (Wang, 1999), and there have been simplified relations such as Bortfeld (1961), Aki & Richards (1980), Shuey (1985), Hilterman (1990) and Mallick (1993), The most influential one is the two-term appro...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01V1/30
Inventor 梁虹巫芙蓉孙建库秦俐黄花香司阳涛文中平杨冬梅陈春兰刘春
Owner BC P INC CHINA NAT PETROLEUM CORP
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