Hydrocarbon Reservoir Characterization Methodologies and Uncertainties as a Function of Spatial Location: A Review from Gigascale to Nanoscale

Abstract

Reservoir characterization is fundamental to hydrocarbon exploration and exploitation due to the intricate nature of geological structures. Nonetheless, uncertainties arise because of the variability in reservoir properties across different spatial locations and scales. Recognizing the significance of spatial location is key to building more dependable models and methods. During the characterization process, data from various sources are scrutinized. This presents issues like time-sensitivity, varying data quality, different measurement scales, discrepancies in resolution, interpretation challenges of qualitative data, and complex mathematical relationships. The uncertainties associated with reservoir characterization are scale-dependent and influence reservoir performance comprehensively. While geostatistical techniques such as kriging and sequential Gaussian simulation aid in managing these uncertainties, achieving a thorough understanding of the spatial effects remains a vibrant research topic. Enhancing model accuracy requires cutting-edge methodologies such as data integration, cooperative modeling, and scale-dependent techniques. Although geostatistical methods provide numerous advantages like including uncertainty estimates and honoring the gathered data, they demand additional data and sometimes involve subjective decisions. The future of reservoir characterization hinges on integrating giga and nano-scale data, supported by data analytics and machine learning. This fusion could revolutionize reservoir comprehension and refine production plans. However, there's a need to address challenges in data systems, computational resources, and mastering machine learning algorithms. For progress, petroleum geoscientists should amalgamate macro and micro-scale data, allocate resources to innovative data technologies, and adopt data analytics and machine learning. Prioritizing spatial uncertainties and fostering cross-discipline collaboration will further optimize production and minimize risks, steering the hydrocarbon industry ahead.