Topological Data Analysis to Solve Big Data Problem in Reservoir Engineering: Application to Inverted 4D Seismic Data

Abstract

Data analysis is one of the most important topics in any industry. In petroleum engineering, the large, complex, and multi-dimensional reservoir data sets (big data) presents a challenge for engineers to study the masses of unstructured information and make decisions. A new approach to analyze complex data is called Topological Data Analysis (TDA) which aims to extract meaningful information from such data. TDA relies on the concept that complex data has shapes where shape has meanings. It analyzes the shape of complex data, identifying clusters and their statistical significance. The objective of this paper is to introduce TDA to reservoir engineering using an example of inverted 4D seismic data for studying reservoir connectivity and compartmentalization. In this paper, we introduce the principles of TDA and discuss its potential in reservoir engineering, which could allow identification of reservoir engineering data behavior, recognition of new opportunities, detection of anomalies and events, and minimizing uncertainties. The TDA procedures are introduced using inverted 4D seismic data set to study reservoir connectivity and compartmentalization. The process to generate and process the data set is explained. Similarity distance function and lenses are defined and used to create TDA graphs for feature identification and analysis. It is shown that TDA is able to predict the compartmentaliztion of the reservoir models with various process configurations. Variance normalized Euclidean and topological neighborhood function are used successfully to compartmentalize the reservoir model. Using normalized input dataset, correlation and principle component analysis also create similar compartments. The success of TDA in discovering meaningful patterns is attributed to the similarity distance function representing the objective of study, one or more lenses exposing the data, and the right combination of input data, similarity distance function and lenses. A promising big data analysis method, TDA, is introduced to reservoir engineering application with principles, procedures and examples. It has been shown that TDA can automatically discover critical intelligence within the 4D seismic data set for studying reservoir connectivity and compartmentalization, which are essential to the accuracy of forecasts and development plans, the validity of reservoir simulation, and the success of performance diagnostics and optimization.

A Comprehensive Review on Big Data for Industries: Challenges and Opportunities

“…kilometer of 3D regional data. Alfaleh et al [19] applied topological data analysis (TDA) to analyze the shape of complex data, and discover clusters and their statistical importance for examining reservoir connectivity and compartmentalization. The application of TDA allows forecasting with high accuracy, new plans for growth, efficiency measurement, and optimization.…”

“…Seismic interpretation [30], analysis [19], Drilling knowledge discovery [68], [83], optimization [43], Drilling cost optimization [77], [34] Production engineering [90], production allocation [21], well productivity [55], surveillance [114],…”

“…Ak et al [14] Adrian et al [15] Alam et al [16] Cadei [17] Desai et al [8] Wang et al [18] Alfaleh et al [19] Alguliyev et al [20] Rollins [21] Chan et al [22] Tsanousa et al […”

Human Centric Digital Transformation and Operator 4.0 for the Oil and Gas Industry

“…Big data analytics has been using a range of tools and techniques, including use of 3D plane to show the relationship between data, science, technology, engineering, and mathematics (STEM) tools, and pattern recognition [100]- [102]. Machine learning tools can reveal the relationship between the recorded data more efficiently, specifically for the recent trend dealing with huge datasets [103].…”