# A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs

## TL;DR

This paper introduces a hybrid physics-based and data-driven spatio-temporal clustering framework for rapid, accurate multiscale modeling of compartmentalized reservoirs, addressing data sparsity and complexity.

## Contribution

It presents a novel hybrid approach coupling non-local physics-based modeling with clustering techniques for reservoir analysis, enhancing speed and accuracy.

## Key findings

- Effective detection of reservoir compartments using spatio-temporal data
- Improved multiscale reservoir modeling accuracy
- Robustness to sparse and noisy data

## Abstract

Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach. In this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome. Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal Clustering; Physics-Based Data-Driven Formulation; Multiscale Modeling