MAGCS: Machine Assisted Geologic Carbon Storage
Authors: Alqassab H. M. , Feng M. , Becker J. A. , Song S. , Mukerji T. 
Published: November 2024

This study presents a groundbreaking geologic modeling technique powered by artificial intelligence (AI), which is executed in a phased approach. We employ generative AI-based models to construct detailed 3D geologic models that are conditioned to well data and 3D seismic information and validated with real field cases. The paper showcases the practical application of this method by creating a model of a fluvial-dominated deltaic saline aquifer, a potential site for CO2 sequestration.

Existing geological modeling methods face limitations in generating heterogeneity structures, encompassing the geometries of lateral geological features such as baffles and barriers, as well issues encountered with stacking patterns. These limitations result in simplistic tank-like models that lead to inaccurate predictions of CO2 behavior in the subsurface. Our approach offers a method of rapidly exploring the uncertainty space associated with subsurface heterogeneity. By capturing the full range of potential heterogeneity associated with our fluvial-dominated deltaic saline aquifer concept, we can constrain expected CO2 plume migration behavior. This can lead to enhanced development plan optimization, better well placement and greater surety that CO2 plume migration will behave as predicted. We thereby stand to reduce overall project uncertainty. Through our enhancement of geological heterogeneity characterization, we also recognize we have the potential to positively influence the efficiency of CO2 storage.

In this study, we take inspiration from other industries (e.g. computer vision, natural language processing, gaming) to leverage the power of AI to deliver high-impact technology in alignment with our business objectives. Our main goal is to use AI to develop geologically realistic heterogeneous reservoir models and stacking patterns, each characterized by distinct flow behaviors through the application of machine learning knowledge. We use advanced deep learning techniques, conditional variants of Generative Adversarial Networks (GANs), to facilitate the sample distribution learning and generation of realistic geological shapes and patterns from conditional inputs and latent spaces. The geologic features utilized by our methods can be generated from process-based models, outcrop analogs and existing models which serve as the foundational training data for our machine learning processes. By using feedstock such as process-based systems or rule- and sketch-based methods as inputs for GANs, we preserve the continuity of flow barriers and gain insights into the previously undiscovered geometries of lateral geologic features. This approach enables direct control of depositional heterogeneity, mimicking the depositional process and thereby eliminating the need for complex geostatistical models.

Our longer-term objective is to model a variety of geological scenarios that encompass the transition from a fluvial system (i.e. braided and meandering rivers) to a shallow-marine system (i.e. wave-dominated and fluvial-dominated deltas). Final models would contain variability that captures the diverse reservoir architectures of the depositional environment modelled, each characterized by distinguishable flow behaviors. In this phased approach, our first proof-of-concept phase focuses on obtaining feedstock data for GANs from object-based modeling of a fluvial-dominated delta. This paper presents the outcomes and insights gained during the first phase of our innovative project.