Source Rock Evaluation from Rock to Seismic Data: An Integrated Machine-Learning-Based Work Flow and Application in the Brazilian Presalt (Santos Basin)

Abstract

The capacity to predict the occurrence and quality of source rocks in a sedimentary basin is of great economic importance in the evaluation of conventional and non-conventional petroleum resources. Direct laboratory examinations of rock samples are the most accurate way to obtain their geochemical properties. However, rock information is usually sparse, and source rocks are often sampled at positions that may not be representative of the average organic content and quality of oil kitchens. This work proposes a work flow supported by machine learning methods (random forest, DBSCAN, and NGBoost) to automate the source rock characterization process to maximize the use of available data, expand data information, and reduce data analysis time. From the automated quality control of the input data through the extrapolation of laboratory measurements to continuous well logs of geochemical properties, culminating in the 3D estimation of these properties, we generate volumes of total organic carbon (TOC) by applying machine learning techniques. The proposed method provides more accurate predictions, reducing uncertainties in the characterization of source rocks and assisting in exploratory decision making. This methodology was applied in the presalt source rocks from Santos Basin (Brazil) and allowed us to quantify the TOC distribution, improving the interpretation of the main source rock interval top and base based only on seismic amplitude data. The result suggests higher TOC values in the northern and western grabens of the studied area and a higher charge risk in the eastern area.

Editorial for the Special Issue “Applied Geophysics in Hydrocarbon Exploration, Energy Storage and CCUS”

“…Moreover, source rocks could be sampled at positions that may not represent the oil kitchens' average organic content and quality. To overcome these challenges, Reis and coauthors [8] propose a seismic interpretation workflow supported by machine learning methods such as random forest, DBSCAN, and NGBoost to automate the source rock characterization methodology from the seismic data. Their technique helps maximize available data, expand information, and reduce data analysis time.…”