Multi-scale geophysical characterization of microporous carbonate reservoirs utilizing machine learning techniques: An analog case study from an upper Jubaila formation outcrop, Saudi Arabia. (June 2023)

• Record Type: Journal Article
• Authors: Ramdani, Ahmad Ihsan; Chandra, Viswasanthi; Finkbeiner, Thomas; Vahrenkamp, Volker
• Is Part Of: Marine and petroleum geology. Volume 152(2023)
• Publication Date: 2023-06
• Subjects: Microprosity -- Arab-D reservoir -- Machine learning -- Outcrop; Submarine geology -- Periodicals; Petroleum -- Geology -- Periodicals; Géologie sous-marine -- Périodiques; Pétrole -- Géologie -- Périodiques; Petroleum -- Geology; Submarine geology; Periodicals; Electronic journals; 551.468
• DOI: 10.1016/j.marpetgeo.2023.106234
• Languages: English
• ISSNs: 0264-8172

Abstract

Abstract: Microporosity hosts a significant portion of the total hydrocarbon volume in the Middle Eastern carbonate reservoirs. An improved understanding of microcrystals' morphology that hosts micropores, their impact on reservoir properties, and their spatial distributions will contribute to a more accurate reservoir quality prediction. This study proposes a methodology that utilizes machine learning approaches for microporosity characterization and integrates a multi-scale dataset ranging from micrometer-scale SEM images to meter-scale seismic attributes to build reservoir porosity models. We tested the methodology on an outcrop in Riyadh, Saudi Arabia, which exposes the upper part of the Jubaila Formation equivalent to the lower part of the Arab-D reservoir. We acquired a 35 m-long core and a 600 m-long 2D seismic line behind the outcrop. Laboratory-scale petrophysical measurements, including porosity, permeability, acoustic velocity, bulk and grain density, and x-ray diffraction, were performed over 106 horizontal core plugs drilled from the core. We quantitatively characterized the morphology and microtextures of micrite crystals using SEM images by performing Random Forest classifications trained on SEM image features. We performed unsupervised classification using Self-Organizing Map (SOM) to all lab-measured properties for data clustering. We investigated potential correlations between SOM clusters with micrite morphology, which resulted in a predictable relationship following the granularity and sphericity of microcrystals with porosity, permeability, and acoustic velocity. It was possible to represent multiple lithofacies with a single log-linear porosity-permeability relationship and a single value of equivalent differential effective medium (DEM) aspect ratio in velocity-porosity space. The inter-relationship between micrite morphology and microporosity in the well was then propagated to reservoir-grid scale using inverse differential effective medium (DEM) of acoustic impedance from inverted seismic data. The methodology developed in this study thus provides a practical way to integrate key sub-grid scale micro-and macro-heterogeneities into reservoir scale property models. Highlights: Comprehensive geophysical microporosity study of the Arab-D reservoir outcrop analog. A methodology that involves machine learning approaches for microporosity characterization and data integration. A practical approach to propagate sub-grid scale micro-and macro-heterogeneities into reservoir scale porosity models. Discuss the relationship between the morphology of microcrystals that hosts microporosity and the petrophysical properties.