Marwah Mufid AlSinan

Title: Marwah Mufid AlSinan: Innovator in Geological Modeling and Machine Learning

Introduction

Marwah Mufid AlSinan is a prominent inventor based in Al Qatif, Saudi Arabia. He has made significant contributions to the field of geological modeling and machine learning, holding a total of 11 patents. His innovative approaches have advanced the understanding of reservoir formations and fracture modeling.

Latest Patents

One of AlSinan's latest patents is a method and system for determining coarsened grid models using machine-learning models and fracture models. This method involves obtaining fracture image data regarding a geological region of interest. It includes determining various fractures in the fracture image data using a first artificial neural network and a pixel-searching process. Additionally, it encompasses determining a fracture model using the fractures, a second artificial neural network, and borehole image data. The method further includes determining various fracture permeability values using the fracture model and a third artificial neural network. Moreover, it involves determining various matrix permeability values for the geological region of interest using core sample data. Finally, the method generates a coarsened grid model for the geological region of interest using a fourth artificial neural network, the matrix permeability values, and the fracture permeability values.

Another significant patent focuses on techniques for determining reservoir formation properties. This includes generating digital models of core samples taken from one or more reservoir formations based on corresponding images of the core samples. The process determines a pore throat size distribution of the core samples based on the generated digital models. It also involves determining corresponding capillary pressure curves and NMR value distributions of the core samples through numerical simulations. The invention generates machine-learning models based on the pore throat size distribution, capillary pressure curves, and NMR value distributions. It adjusts these models with reservoir data and generates adjusted capillary pressure curves and NMR value distributions. Ultimately, it determines a reservoir formation specific pore throat size distribution from the adjusted data.

Career Highlights

Marwah Mufid AlSinan has worked with notable organizations, including the Saudi Arabian Oil Company and King Abdullah University of Science and Technology. His experience in these institutions has allowed him to apply his innovative ideas in practical settings, contributing to advancements in the oil and gas industry.

Collaborations

AlSinan has collaborated with esteemed colleagues such as Hyung Tae Kwak and Jun Gao. These partnerships have fostered a collaborative environment that enhances the development