Geosystems and Geoenvironment (Feb 2023)
Classification of reservoir quality using unsupervised machine learning and cluster analysis: Example from Kadanwari gas field, SE Pakistan
Abstract
Understanding geological variance in a proved reservoir requires accurate as well as exact characterization of lithological facies. In the Kadanwari gas field, machine learning (ML) classification algorithms have been used to forecast facies on such an accessible dataset. The goal is to increase the reliability of facies categorization using a rigorous application of machine learning. In the current study to identify lithofacies, we have used the self-organizing map (SOM) and crossplot techniques. In the classification of the reservoir, recognition of lithofacies is the main piece of work. It is expensive to identify lithofacies with conventional methods from core data, and it is challenging to extend this application to non-cored wells. This research provides a less expensive method for the systematic and objective recognition of lithofacies through well-log data by Kohonen SOM. The SOMs are human-made neural networks that do not need surveillance and map the input space into groups in the structure as topology is arranged according to the input data changes. The results of SOM and crossplot indicates that the zone of interest is mainly composed of sandstone, shaly sandstone, shale with diminutive amount of carbonates. The cluster analysis approach has been utilized to categorize the reservoir rock groups in the Cretaceous reservoir for the Kadanwari gas field by analyzing the variance of reservoir properties data that are forecast by examining well log dimensions. Four groups of reservoirs were concluded, each of which was internally identical in petrophysical properties but distinct from the others. The reservoir mainly composed of sandstone is graded as excellent reservoir, while shale is graded as poor reservoir.
