The New Technique Applied Research In Logging Reservoir Evaluation

The logging reservoir evaluation is one of the main contents of the remaining oil research. This dissertation develops the new technique applied research in logging reservoir evaluation based on the research project "The middle-high containing water oil field's remaining oil research of conglomerate petroleum deposit in Keshangzu, No.8 district, Xinjian Karamay oil field", the research includes the logging interpretation, the reservoir evaluation, inhomogeneity, flow unit, 3D modeling, and so on.The neural network application research is the main method used in the logging interpretation. Based on analyzing the BP neural network's algorithm and combing the feature of the logging, a scheme was put forward. It adopted variable learning rate, added a dynamic term and a steep factor, limited the S-function's output amplitude. In taking the learning samples, we eliminated the anomaly point, reduced the number of the samples with similar feature, and used the reconstruction samples. Through logging data analysis, rock depth immigration, saturation correction, some representative learning samples were selected, the model of the statistical analysis and the BP-neural network were established, and this model is applicable to the studied area.The reservoir evaluation is the major part of this dissertation. It is composed of two parts, the gray-theory and the CP-neural network. Using the new concept of gray-theory, the logging-geology database was built. The evaluation feature parameter, including the conglomerate petroleum deposit's lithology, geophysical properties and oiliness, was selected, matched, modeled and extracted, and used to establish the interpretation standard and weight coefficients. Then the evaluation result was got by using the matrix transformation and the max subjection principle. The CP-neural network is used firstly in the logging reservoir evaluation. For the features of logging reservoir evaluation, the CP-neural network is modified by confusing the learning-samples, adding the error processing and test phase, and supposing the experienced cell number of the middle layer in the CP neural network. Based upon the ideal above, the CP-neural network's software was developed. It works well in the research. Combining the gray-theory with the CP-neural network, the logging reservoir evaluation in the studied area was complished. The coincidence between the evaluation and the oil testing is about 85%.One of the obvious features of the studied area is the reservoir's inhomogeneity. The fractal theory was used to deal with the problem. The plot in plane view of each thin layer's porosity, perm, oil saturation, net thickness and the separate/inter layer's thickness was obtained. This will help to understand the space distribution of the parameters, and provide evidences for the oil field development.In order to get more information about the reservoir's inner flow liquid, someresearch were done on the reservoir flow unit. Through the reservoir pore geometry, the flow zone index (FZI) was adopted to identify the flow unit. The flow unit is divided into 5 classes with the cluster analysis combined with its lithology and geophysical properties. The unlifting core well's FZI was forecast by statistical analyzed the lifting core well's FZI. Combined with the result of the micro-Sedimentary facies, each thin layer's flow unit distribution was obtained and descripted. This will help to get more information on the reservoir lithology, geophysical properties, inhomogeneities and the thin layer's connectivity.Finally, based on the research above, the 3D model was built. Using the Gridstat's software to build the 3D model of the reservoir's porosity, perm, oil saturation and mud content. Two cross-sections were acquired, one from Well-151 to Well-8215, another from Well-8237 to Well-8239. Combined with the Kriging techniques and the 3D modeling result, the petroleum deposit reserve was calculated two times by the grid-integral. Compared with the calculated result of the perforating technique (1266.0#10...