2012Journal of Oil and Gas TechnologyRequires access

The Reservoir Features and Logging Capacity Evaluation of Chang4+5 Reservoir in Huanjiang Oilfield

Yirong Zhang

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Abstract

It was great significant on the rational development and productivity establishment of oil and gas reservoirs for researching reservoir features.The geological parameters extracted by logging information could reflect the productivity of the reservoir to a certain extent.In view of the low porosity and extral-low,ultra-low permeability of Chang 4+5 Reservoir in Huanjiang Oilfield,based on the reasonable classification of reservoir,the key geological parameters impacting the productivity were evaluated by fully using logging information.By using weighted reservoir coefficient method,logging multi-parameter calibration method,regression analysis method based on multi-parameter,the natural productivity of Chang4+5 Reservoir in Huanjiang Oilfield was predicted and evaluated quantitatively and qualitatively.Through comparative analysis of the predicted results and the actual test results,it shows that the predicted results based on logging data are well correlated to the actual test results,therefore it is determined that the productivity prediction model is reliable,by which the goal of predicting formation test production,choosing oil-test strata and guiding the exploration and development of oilfield is obtained by using well logging data.

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What this paper is about

It was great significant on the rational development and productivity establishment of oil and gas reservoirs for researching reservoir features.The geological parameters extracted by logging information could reflect the productivity of the reservoir to a certain extent.In view of the low porosity and extral-low,ultra-low permeability of Chang 4+5 Reservoir in Huanjiang Oilfield,based on the reasonable classification of reservoir,the key geological parameters impacting the productivity were evaluated by fully using logging information.By using weighted reservoir coefficient method,logging multi-parameter calibration method,regression analysis method based on multi-parameter,the natural productivity of Chang4+5 Reservoir in Huanjiang Oilfield was predicted and evaluated quantitatively and qualitatively.Through comparative analysis of the predicted results and the actual test results,it shows that the predicted results based on logging data are well correlated to the actual test results,therefore it is determined that the productivity prediction model is reliable,by which the goal of predicting formation test production,choosing oil-test strata and guiding the exploration and development of oilfield is obtained by using well logging data.

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Available abstract

It was great significant on the rational development and productivity establishment of oil and gas reservoirs for researching reservoir features.The geological parameters extracted by logging information could reflect the productivity of the reservoir to a certain extent.In view of the low porosity and extral-low,ultra-low permeability of Chang 4+5 Reservoir in Huanjiang Oilfield,based on the reasonable classification of reservoir,the key geological parameters impacting the productivity were evaluated by fully using logging information.By using weighted reservoir coefficient method,logging multi-parameter calibration method,regression analysis method based on multi-parameter,the natural productivity of Chang4+5 Reservoir in Huanjiang Oilfield was predicted and evaluated quantitatively and qualitatively.Through comparative analysis of the predicted results and the actual test results,it shows that the predicted results based on logging data are well correlated to the actual test results,therefore it is determined that the productivity prediction model is reliable,by which the goal of predicting formation test production,choosing oil-test strata and guiding the exploration and development of oilfield is obtained by using well logging data.

Key concepts: Logging, Petroleum engineering, Reservoir modeling, Well logging, Productivity, Productivity model, Permeability (electromagnetism), Geology

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