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Study On Productivity Prediction Methods Of Nearly Tight Reservoir

Posted on:2017-03-28Degree:MasterType:Thesis
Country:ChinaCandidate:J ChenFull Text:PDF
GTID:2180330488968534Subject:Geological Engineering
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Oil and gas production is a reflection of a well producing oil and gas capacity size, and also an essential task for exploration and development of oil fields, and there are many factors about the capacity, you want to accurately predict reservoir of oil and gas production is very difficult. Productivity prediction is the use of oil and gas reserves as well as various parameters of production,the prediction of production well is providing the basic parameters for the oil and gas field development.Most oil fields is the direct use test data to predict oil and gas reservoir capacity. By logging data to predict production method is not widely used. Reservoir engineering field has a variety of reservoir capacity evaluation and prediction methods. Most of them are pressure tested and production of oil and gas system test data to predict capacity. How to use logging data to predict the capacity is relatively small.Oil production capacity of the reservoir is a dynamic index, that is to say" every day the change of a single production wells is because of various factors" The use of logging reservoir evaluation tools to obtain reservoir parameters mainly reflects a static reservoir characteristics, it can not reflect the dynamic characteristics of the reservoir. Therefore, the use of the main objectives logging reservoir capacity study is able to use these parameters to predict the static reservoir capacity.Log data can be obtained main reservoir porosity, permeability and water saturation and irreducible water saturation. These arguments do not directly reflect the reservoirs’productivity, can only be explained qualitatively reservoir is oil, water or gas reservoir layer. This article is on how to use less oil well logging data binding test data to predict reservoir capacity. The Area is a typical low porosity and low permeability sandstone reservoirs, heavy clay content, reservoir types, distribution complex. This study is based on rock physics experiments, study of low porosity and permeability gas reservoir area XDue to the complexity of reservoir microstructures, low oil and gas production and sensitivity, to carry out this research will help to close tight reservoirs systematically recognize and understand the impact on productivity. Experimental results presented herein to MDT and DST test data, mercury data, core porosity, etc., based on the recent tight reservoir structural features of porosity, capacity factors and other in-depth study, re-use neural network capacity forecast.1 production forecast, porosity, permeability and saturation predicting reservoir production capacity is a key factor, and the region is a low porosity and low permeability, relatively low permeability, the use of MDT test data obtained effective reservoir permeability of productivity prediction of great help.2 reservoir capacity is determined by the size of the reservoir to its own conditions, by logging data to calculate the water saturation of the reservoir and irreducible water saturation, through field testing data to determine the skin factor and reservoir oil reservoir and effective radius the thickness of the reservoir capacity factors determine the impact.3 In this paper, mainly through logging data to classify the reservoir, mainly using FZI value, combined with the data on the mercury reservoir classification, then rice oil index to indicate the size of the reservoir capacity, the establishment of different reservoirs different production forecasting model.4 In the classification based on the use of rice oil index to predict the size of production capacity, while the use of neural network models for different reservoirs select different training samples to factors affecting the reservoir capacity for the input layer to obtain reservoir capacity layer.Through research, the method of production forecasts and evaluations provide some reference and basis.
Keywords/Search Tags:productivity prediction, low porosity and permeability, MDT, neural networks, reservoir classification
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