Rock type based-estimation of pore throat size distribution in carbonate reservoirs using integrated analysis of well logs and seismic attributes

Abstract

Depositional setting characterization is one of the most important tasks in petroleum basin analysis. In this regard, artificial intelligence has emerged as a game-changer in the field of oil reservoir characterization, offering a myriad of benefits that significantly enhance the exploration and production processes within the oil and gas industry. Artificial Intelligence driven algorithms can efficiently process geological and geophysical data, well logs, and seismic information, allowing for a more comprehensive understanding of reservoir properties. To obtain more appropriate image of high reservoir quality zones, a case study was performed by integrating 3D seismic and well data related to on onshore oilfield, west of Iran. Supporting data were acquired from existing geochemical analyses of scanning electron microscope, thin-section investigation, and special core analysis laboratory measurements related to three wells of the studied oil field. The methodology developed in this study consists of three main phases, at the first step a complete thin section analysis is done to identify the main facies of the studied reservoir. Four mains microfacies and representative sedimentary environment were identified including: (a) Foraminifera bioclastic wackestone (Mid ramp-Distal), (b) Benthic foraminifera bioclast peloid wackestone to packstone (Mid ramp-Proximal), (c) Coated grains bioclast packstone to grainstone (Inner ramp-Shoal), (d) Bioclast Peloid wackestone (Inner ramp-Lagoon). To create a continuous pore throat size log, porosity and permeability logs were initially generated through petrophysical evaluation and artificial neural network analysis, achieving an accuracy of R² = 0.95 for porosity and R² = 0.84 for permeability. Subsequently, the pore throat size log was generated using the Winland equation, and the results were calibrated with pore throat sizes calculated from capillary pressure data analysis using the Washburn equation. Two different approaches including FZI and K-means clustering methods are also employed to recognize Hydraulic Flow Units. According to the Sum of Squared Errors (SSE) plot of the K-means algorithm, beyond three clusters, the reduction in SSE becomes marginal, suggesting that three clusters suit the dataset appropriately. In the next step, the sparse spike algorithm was used to generate a 3D acoustic impedance cube. Finally, post-stack seismic attributes, including the inverse of acoustic impedance, instantaneous frequency, a filter of 15/20–25/30, and amplitude-weighted phase, were selected to create a 3D pore throat size cube using a Probabilistic Neural Network, demonstrating a strong correlation of R² = 91. The resulting pore throat size cube effectively illustrates that the Ilam-Upper and Ilam Main zones, which include HFU 3, exhibit high reservoir quality, with porosity, permeability, and mean pore throat size values of 16%, 20–67 mD, and 3–6 microns, respectively. In summary, the integration of acoustic impedance in oil reservoir characterization revolutionizes how we extract and manage hydrocarbon resources, driving efficiency, cost-effectiveness, and sustainability.