Integrated Reservoir Modelling using Spatio-Temporal Unsupervised Learning and Integrated Visualization

Abstract

Probabilistic modelling is one of the most frequently used methods in reservoir simulation to manage uncertainties and assess their impact on reservoir behavior/cumulative production. However, depending on the extent of the uncertainty, 100s of scenarios can be generated leaving engineers unable to meaningfully analyze this data. To remedy this an unsupervised machine learning based workflow was developed to identify unique scenarios which was then paired with an integrated dashboard to enable rapid and deep analysis. A case study was done using data from a Shell operated gas field in the North Sea. Data was first mined from 480 history matched scenarios using python; out of which 20 unique clusters were identified through K-Means clustering of pressure and saturation changes with time in each gridblock. This meant that the team had to look only at 20 scenarios instead of 480 to understand the effect of different inputs on pressure and saturation response. For enhanced analysis, an integrated visualisation dashboard was created to visualize pressure and saturation changes, production profiles and connect them back to input parameters The new methodology enabled the team to integrate different aspects of reservoir modelling from static to dynamic to surface constraints on a single dashboard, making it possible to find patterns in large volumes of data which was previously not possible. For example, a cluster was identified which had high water movement; upon inspection of input parameters it was seen that late life recovery was significantly different in this cluster as compared to others. Being able to visualize different properties of multiple scenarios simultaneously at both group and grid level is a very powerful tool that not only generates insights but significantly reduces analysis time and helps in quality checking property modelling and grid behavior. The developed workflow is quite generic in nature, capable of working with various simulators and can be extended to assessing history match quality in Assisted History Matching (AHM) and multi-scenario modelling. Key parameters impacting different scenarios were identified and the team observed 10x reduction in time and significant reduction in manpower requirements through the new approach

Comparative evaluation of multi-basin production performance and application of spatio-temporal models for unconventional oil and gas production prediction

“…Wigwe et al (2019a, b) presented both spatial and neural network techniques to analyze Bakken oil production while Zargari and Mohaghegh (2010) showed an application of machine learning models for the Bakken field development planning. Simha et al (2019) integrated spatio-temporal unsupervised learning method with reservoir simulation to identify a unique scenario for assessing the impact of uncertainties on production. Although the modeling workflow presented in this paper is similar for other data analytic applications, the specific methods implemented, and their formulation is not native to the oil and gas industry.…”