Intelligent prediction of logging parameters of tight sandstone gas reservoirs based on knowledge-driven chart constraints

Tight sandstone gas resources in China have great potential and are important options of increasing gas reserves and production. However, tight sandstone reservoirs are characterized by diverse space types, significant variation in lateral and vertical directions, and complicated relations among four kinds of properties. Such reservoirs need to be logged with a variety of tools, but have not been logged sufficiently. Moreover, the application of conventional logging techniques in evaluating tight reservoir parameters is challenging and insufficient. Taking the tight gas of Jinqiu and Tianfu gas fields as an example, this paper constructs a main line of structural block-oil and gas field-oil and gas reservoir-logging interpretation chart, and establishes a knowledge graph of reservoir logging parameter interpretation of tight sandstone gas. In addition, the neural network algorithm is applied to process the sample data and constrain the model results. In this way, a chart-constrained artificial intelligence prediction model of reservoir logging parameters is established, and the intelligent prediction of reservoir logging parameters under the two-way driving of expert experience and data is realized. And the following research results are obtained. First, expert experience chart information is incorporated in the new intelligent model, and an intelligent parameter prediction method under the two-way driving of expert experience and data is established. They greatly enhance the new model's knowledge understanding and application capabilities in the field of well logging. Second, based on conventional logging curves, multi-dimensional features are mined through feature processing, and new curves are derived, which together with conventional curves are input to enhance model training, so as to improve the accuracy of the interpretation model. Third, the actual application results show that the errors between the porosity and permeability calculated by the intelligent reservoir parameter prediction method of tight sandstone gas under the constraint of knowledge-driven chart and those derived from core analysis are 7.9% and 15% respectively, and the error between the calculated water saturation and the closed coring saturation is only 5%. In conclusion, the intelligent reservoir parameter prediction technology for tight sandstone gas reservoirs can solve such problems as heavy manual evaluatiob of old wells and inconsisitent logging interpretation standards. And it can realize fast and efficient intelligent logging evaluation and optimal potential selection, which will powerfully promote the deep application of artificial intelligence in the field of well logging. © 2024 Natural Gas Industry Journal Agency. All rights reserved.