Numerical simulation results are the basis of numerous oil and gas field developments. We based the numerical simulation models (or dynamic models) on 3D geological models. We constructed a geological model using core and log data obtained from wells as inputs to create a reservoir prototype. This paper describes the applications of artificial intelligence (AI) algorithms for parameterization of static and dynamic modeling processes. Accordingly, a hypothetical 3D geological model was created, and porosity and permeability were distributed using sequential Gaussian simulation. Then, Petro-physical rock types (PRT) were defined in the 3D space as a function of porosity and permeability using a hypothetical Winland's R35 equation. Finally, hypothetical saturation-height functions (SHFs) were defined for different PRTs to populate water saturation in the 3D geological model. Subsequently, some wells were randomly defined in the 3D model to obtain the logs of porosity, permeability, SHF, PRT, repeat formation tester pressure (RFT), and datum pressures that are used in this study. A multivariate Gaussian regression was applied for anomaly detection, while core porosity and permeability were filtered. Subsequently, a fixed window average was used to detect the boundaries of core data stationarity and propose the optimum reservoir zone required to describe the internal heterogeneities of the reservoir. Then, we deployed the k-means clustering algorithm to determine the PRT and saturation height function (SHF) based on the core and log data derived from the hypothetical geological model. Finally, we used the clustering-based pattern recognition to cluster well datum pressures into homogeneous groups and create a connected reservoir region CRR map to be used as an input in the 3D permeability distribution. Our results demonstrate the value of additional diagnostics that can be used in conjunction with the traditional semi-log plot of porosity and permeability. This additional diagnostic approach is a semi-log plot of permeability versus depth, which can help check whether intra-reservoir heterogeneities observable in core data have been preserved in the 3D model. In our case, a 3D model created using the core and log data from the hypothetical model and honoring the internal reservoir architecture resulted in a better history match regarding the hypothetical geo-model's RFT pressure signature. Our results further demonstrate that PRT and SHF derived from k-means clustering are sufficiently similar to those of the hypothetical model. Time series anomaly filtering of pressures helped detect incorrect well data that may otherwise have gone unnoticed. Using the nearest-neighbor property distribution resulted in a geological model whose diagnostic plots indicated an excellent match with core data and allowed a better assessment of modeling uncertainties. The ML approaches presented in this study could help obtain data-derived PRT and SHF to complement Winland's interpretation when Mercury Injection Capillary Pressure (MICP) experiments are limited or unavailable, saving both time and cost. Using the fixed window averaging helps optimize the geological model zone assessment, resulting in a better intra-reservoir architecture. Finally, we derive insights into a more efficient core acquisition plan.
|Title of host publication||SPE Symposium: Leveraging Artificial Intelligence to Shape the Future of the Energy Industry|
|Publisher||Society of Petroleum Engineers|
|Publication status||Published - 19 Jan 2023|
|Event||SPE Symposium: Leveraging Artificial Intelligence to Shape the Future of the Energy Industry - Al Khobar, Saudi Arabia|
Duration: 17 Jan 2023 → 18 Jan 2023
|Conference||SPE Symposium: Leveraging Artificial Intelligence to Shape the Future of the Energy Industry|
|Period||17/01/23 → 18/01/23|