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Method Of Productivity Prediction In Sandstone Reservoirs With MDT Data

Posted on:2015-12-29Degree:MasterType:Thesis
Country:ChinaCandidate:R LiuFull Text:PDF
GTID:2180330431472633Subject:Earth Exploration and Information Technology
Abstract/Summary:PDF Full Text Request
The evaluation of productivity prediction in sandstone reservoir is always a fundamental task in the field of oil and gas exploration and development. Productivity is a composite indicator of the dynamic characteristics to oil and gas reservoirs, it is a dynamic equilibrium in the process of mutual constraints between oil and gas reservoir production potential and a variety of influencing factors. Productivity analysis is combined with the reservoir and production data of oil and gas wells, analyze the influencing factors of output in producing wells, and give future production of a more appropriate predication. Therefore, Not only can check the results of oil and gas exploration, but also can for provide reference for the right development plan and the scientific development of oil and gas reservoirs, in order to ultimately improve the economic efficiency of oil and gas exploration and development.Currently, the reservoir productivity study has become one of the key issues of domestic scholars caring. In terms of geophysical well logging, there are key factors(effective porosity, permeability, oil saturation and effective thickness etc) to affect the productivity of the main evaluation parameters to establish a mathematical model to predict sandstone reservoir productivity, combining logging data to predict Repeat Formation Tester productivity. The evaluation of productivity prediction in sandstone reservoir by well logging data studies the relationship between static parameters and dynamic productivity, which is trying to through a variety of methods to achieve the shift from static to dynamic.In the area of reservoir engineering, there have already been multiple methods of production productivity of oil and gas reservoir evaluation and prediction, but they mainly use the oil and gas wells system test data to calculate, such as the formation pressure, the bottom of the well flow stress and the test production etc, which just consider the production productivity of one well or one productive reservoir the methods of using logging information to evaluate and forecast reservoir productivity are still deficient.Building a dynamic model of sandstone reservoir production by using of single-phase flow, multiphase flow, a combination of single-phase flow and multiphase flow, Jones and Fetkovich research methods. The effects of Orkiszewski, Beggs-Brill, Hagedorn&Brown adaptation methods in the wellbore pressure profile, which establish a suitable outflow dynamic model in Xinjiang oil reservoirs. Based on Seepage flow mechanics theory, building an interpretation method with wireline formation tester data, to provide the formation parameters such as formation pressure, effective permeability, skin factor for productivity prediction, and to provide scientific basis for productivity prediction model parameter optimization.Building up the fluid high pressure properties calculation methods and productivity calculation methods in certain work system to determine the actual oil production so that can improve the accuracy of predicting oil production.Studying the method of sandstone reservoirs productivity prediction based on wireline formation tester data in this paper, compiled a set of seamlessly articulate software modules with Lead platform. By processing Xiayan, Cai508and Dixi region12wells,38different layers (including oil layer, oil-water layer, gas layer and gas-water layer) wireline formation tester data in Xinjiang oilfield. By comparing productivity prediction result and test oil conclusion, there are30layers conform to the result, which the compliance rate reach more than78percent. The proposed method is applicable in Xinjiang Oilfield, and the software is reliable, which has a promotional value.
Keywords/Search Tags:sandstone reservoir, wireline formation tester, productivity prediction, model, software module
PDF Full Text Request
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