Hydrocarbon reserve evaluation is the major concern for all oil and gas operating companies. Nowadays, the estimation of oil recovery factor (RF) could be achieved through several techniques. The accuracy of these techniques depends on data availability, which is strongly dependent on the reservoir age. In this study, 10 parameters accessible in the early reservoir life are considered for RF estimation using four artificial intelligence (AI) techniques. These parameters are the net pay (effective reservoir thickness), stock-tank oil initially in place, original reservoir pressure, asset area (reservoir area), porosity, Lorenz coefficient, effective permeability, API gravity, oil viscosity, and initial water saturation. The AI techniques used are the artificial neural networks (ANNs), radial basis neuron networks, adaptive neuro-fuzzy inference system with subtractive clustering, and support vector machines. AI models were trained using data collected from 130 water drive sandstone reservoirs; then, an empirical correlation for RF estimation was developed based on the trained ANN model’s weights and biases. Data collected from another 38 reservoirs were used to test the predictability of the suggested AI models and the ANNs-based correlation; then, performance of the ANNs-based correlation was compared with three of the currently available empirical equations for RF estimation. The developed ANNs-based equation outperformed the available equations in terms of all the measures of error evaluation considered in this study, and also has the highest coefficient of determination of 0.94 compared to only 0.55 obtained from Gulstad correlation, which is one of the most accurate correlations currently available.

In deep hydrocarbon development wells, cement slurry with high density is required to effectively balance the high-pressure formations. The increase in the slurry density could be achieved by adding different heavy materials. In this study, the effect of the weighting materials (barite, hematite, and ilmenite) on the properties of Saudi Class G cement matrix of vertical homogeneity, compressive strength, porosity, and permeability was evaluated. Three cement slurries were weighted with barite, hematite, and ilmenite, and cured at 294 °F and 3000 psi for 24 h. All slurries have the same concentration of the different additives except the weighting material. The amount of weighting material used in every slurry was determined based on the targeted density of 18 lbm/gal. The results of this study revealed that the most vertically homogenous cement matrix was the ilmenite-weighted sample with a vertical variation of 17.6% compared to 20.2 and 24.8% for hematite- and barite-weighted cement, respectively. This is attributed to the small particle size of the ilmenite. The medical computerized tomography (CT) scan confirmed that the ilmenite-weighted sample is the most homogeneous, with a narrow range of density variation vertically along the sample. Hematite-weighted cement showed the highest compressive strength of 55.3 MPa, and the barite- and ilmenite-weighted cement compressive strengths are each 18.4 and 36.7% less than the compressive strength of the hematite-weighted cement, respectively. Barite-weighted cement has the lowest porosity and permeability of 6.1% and 18.9 mD, respectively. The maximum particle size of ilmenite used in this study is less than 42 μm to ensure no abrasion effect on the drilling system, and it minimized the solids segregation while maintaining a compressive strength that is higher than the minimum acceptable strength, which is the recommended weighting material for Saudi Class G cement.