Optimizing reservoir recovery depends on an in-depth understanding of natural geological complexity to predict reservoir behavior. Understanding the difference between producible oil and non-movable oil zones is important, which will aid in the refinement of the design of future wells. During the mature life cycle of the Maastrichtian carbonate reservoir, it was observed that some wells would not perform optimally, while others would experience a significant drop in production. By analyzing petrophysical and production data, the reservoir was found to contain hydrocarbons consisting primarily of heavy oil and stringers of light oil. Based on reservoir characterization and after assessing the production profile to understand the hydrocarbon behavior, this study was performed to identify and distinguish movable oil zones from non-movable oil zones. Conventionally, expensive intervention methods, such as running modular dynamics formation tester (MDT), nuclear magnetic resonance (NMR) logs, and production logging tools (PLT), are used to determine the oil viscosity (API) and identify contribution zones from the entire hydrocarbon interval. However, using these methods results in increased operational costs and reduced production. This study proposes an alternative approach using resistivity logs to identify and distinguish between movable and non-movable hydrocarbon zones to improve reservoir management. The concept behind this method depends on the resistivity logs validated using MDT and PLT data. A shallow resistivity reading higher than a deep resistivity reading indicates that hydrocarbons were not flushed (unmoved) by invasion. Thus, the zone contains unproducible hydrocarbon reserves. The resistivity cut-off value was estimated based on the PLT and MDT data to identify movable oil intervals. In all the wells analyzed, there was a good correlation among the calculated zone thickness, core data, sampling data, and mud logs. Dielectric logs were run in a couple of key wells, which enabled the Sxo estimation independent of resistivity. Additionally, the Sxo obtained supports the fluid interpretation. Productive zones were accurately identified for each well, and recompletions were made to produce from these bypassed opportunities. The proposed method is robust with respect to environmental corrections, not contingent on MDT, NMR, and PLT knowledge, and can be carried out without halting production.

The South Fuwaris Field is located in the Partitioned Zone (PZ) between Saudi Arabia and Kuwait and is operated jointly by Saudi Arabian Chevron (SAC) and Kuwait Gulf Oil Company (KGOC). South Fuwaris produces light crude (24 0 API) from two prolific heterogeneous carbonate reservoirs: Ratawi Limestone and Ratawi Oolite. The field has been developed using vertical and horizontal wells with all wells on electric submersible pumps.Fit-for-purpose static and dynamic models were constructed to validate the Original Oil In Place (OOIP) and to map the distribution of the remaining oil potential in order to devise an optimized development plan for both carbonate reservoirs. This paper illustrates the steps used to build the dynamic model and adopted workflows to identify new infill and waterflood opportunities including the evaluation of pattern versus peripheral injection and optimize placement and completion strategies of vertical and horizontal wells.The paper also presents the processes used to narrow the uncertainty range in Original Oil-Water-Contact (OOWC) surfaces, historical dump flooding volumes, aquifer size and flux, and Pressure Volume Temperature (PVT) data in order to reach a reasonable and defendable history match with an acceptable range of errors and deliver a reliable forecasting tool. Moreover, the paper demonstrates how the reservoir simulation work was efficiently utilized as a mentoring platform for young professional in reservoir simulation and earth science.

The discrimination of shale vs. non-shale layers significantly influences the quality of reservoir geological model. In this study, a novel approach was implemented to enhance the model by creating Pseudo Corrected Gamma Ray (CGR) logs using Artificial Intelligence methods to identify the thin shale beds within the reservoir. The lithology of the carbonate reservoir understudy is mostly composed of dolomite and limestone rock with minor amounts of anhydrite and thin shale layers. The identification of shale layers is challenging because of the nature of such reservoirs. The high organic content of the shales and the presence of dolomites, particularly the floatstones and rudstones, can adversely affect the log quality and interpretation and may result in inaccurate log correlations, overestimating/ underestimating Original Oil In Place (OOIP) and reservoir net pays. In such cases, Corrected Gamma Ray (CGR) curves are typically used to identify shale layers. The CGR curve response is due to the combination of thorium and potassium that is associated with the clay content. The difference between the total GR and the CGR is essentially the amount of uranium-associated organic matter. Because of the very limited number of CGR logs in this reservoir, Artificial Intelligence (AI) approach was used to identify shale volume across the entire reservoir. Synthetic CGR curves were generated for the wells lacking CGR logs using AI methods. Resistivity, Density, Neutron and total GR logs were used as inputs while CGR was set as the target. Five wells that have CGR logs were used to train the model. The created pseudo logs were then used to identify shale layers and could also be used to correct effective porosity logs. After statistical analysis of the data, two different Artificial Intelligence Techniques were tested to predict CGR logs; Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). A Sugeno-type FIS structure using subtractive clustering demonstrated the best prediction with correlation coefficient of 0.96 and mean absolute percentage error (MAPE) of 20%. The resulting synthetic CGR curves helped identify shale layers that do not extend over the entire reservoir area and ultimately correct the effective porosity logs in the reservoir model. Porosity was primarily obtained from the neutron-density logs which results in very high porosity measurements across the shale layers. This study shows a new workflow to predict shale layers in Carbonate reservoirs. The created pseudo CGR logs would help predict shale and is an added-value data that could be incorporated into the Earth model.