Deciphering well connectivity with diagnostic signal processing techniques

Abstract

Data mining has become increasingly crucial in deciphering reservoir dynamics, given that operators currently acquire an enormous amount of data. These data contain valuable information about subsurface processes. Interwell connectivity is one of the most significant aspects of subsurface characterization that can impact a project's success. In this study, we present novel techniques to quantify and monitor interwell communication by applying signal processing methods to observe and derive well-based measurements. A large dataset from the Permian Basin constituted the primary content of this investigation.We constructed a suite of realistic reservoir models under varying conditions involving multiple producing and water injection wells. More than 40 static and dynamic parameters including permeability, porosity, water saturation, fluid properties, and rock-fluid interaction terms are varied using experimental designs to capture realistic uncertainty. Waterflood scenarios are modeled using streamline simulation to infer injector-producer pair connectivity, pattern allocations, drainage efficiency, and their evolution period. These injector-producer variables are analyzed using numerous signal processing methods, including cross-correlation, time-lag correlation coefficient, coherence, and periodogram, among others. Well variables examined involve pressure, rate, and their derivative functions.The objectives of this study include: (1) Examining and devising several signal-processing techniques; (2) Identifying informative well data to provide information of communication between injectors and producers over time; (3) Deducing connectivity measures and systematically evaluate their efficacy in characterizing subsurface connectivity picture.The approaches reveal new insights into determining reservoir communication metrics. The proposed methods have the potential for implementation in the automated virtual flow-metering system. These methods also helped identifying the predominant injector-producer pairs and making operational decisions at any stage of any waterflood and enhanced recovery projects.