Accurate estimation of crude oil Bubble Point Pressure (Pb) plays a vital rule in the development cycle of an oil field. Bubble point pressure is required in many petroleum engineering calculations such as reserves estimation, material balance, reservoir simulation, production equipment design, and optimization of well performance. Additionally, bubble point pressure is a key input parameter in most oil property correlations. Thus, an error in a bubble point pressure estimate will definitely propagate additional error in the prediction of other oil properties. Accordingly, many bubble point pressure correlations have been developed in the literature. However, they often lack accuracy, especially when applied for global crude oil data, due to the fact that they are either developed using a limited range of independent variables or developed for a specific geographic location (i.e., specific crude oil composition). This research presents a utilization of the state-of-the-art Bayesian optimized Least Square Gradient Boosting Ensemble (LS-Boost) to predict bubble pointpressure as a function of readily available field data. The proposed model was trained on a global crude oil database which contains (4800) experimentally measured, Pressure–Volume–Temperature (PVT) data sets of a diverse collection of crude oil mixtures from different oil fields in the NorthSea, Africa, Asia, Middle East, and South and North America. Furthermore, an independent (775) PVT data set, which was collected from open literature, was used to investigate the effectiveness of the proposed model to predict the bubble point pressure from data that were not used during the model development process. The accuracy of the proposed model was compared to several published correlations (13 in total for both parametric and non-parametric models) as well as two other machine learning techniques, Multi-Layer Perceptron Neural Networks (MPL-ANN) and Support Vector Machines (SVM). The proposed LS-Boost model showed superior performance andremarkably outperformed all bubble point pressure models considered in this study.

The accurate estimation of reservoir porosity plays a vital role in estimating the amount of hydrocarbon reserves and evaluating the economic potential of a reservoir. It also aids decision making during the exploration and development phases of oil and gas fields. This study evaluates the integration of artificial intelligence techniques, conventional well logs, and core analysis for the accurate prediction of porosity in carbonate reservoirs. In general, carbonate reservoirs are characterized by their complex pore systems, with the wide spatial variation and highly nonlinear nature of their petrophysical properties. Therefore, they require detailed well-log interpretations to accurately estimate their properties, making them good candidates for the application of machine learning techniques. Accordingly, a large database of (2100) well-log records and core-porosity measurements were integrated with four state-of-the-art machine learning techniques (multilayer perceptron artificial neural network, MLP-ANN; Gaussian process regression, GPR; least squares gradient boosting ensemble, LS-Boost; and radial basis function neural network, RBF-NN) for the prediction of reservoir porosity. The well-log data used in this study include sonic acoustic travel time, Gamma-ray, and bulk density log records, which were carefully collected from five wells in a carbonate reservoir. This study revealed that all the artificial intelligence models achieved high accuracy, with R-squared values exceeding 90% during both the training and blind-testing phases. Among the AI models examined, the GPR model outperformed the others in terms of the R-squared values, root-mean-square error (RMSE), and coefficient of variation of the root-mean-square error (CVRMSE). Furthermore, this study introduces an artificially intelligent AI-based correlation for the estimation of reservoir porosity from well-log data; this correlation was developed using an in-house, Fortran-coded MLP-ANN model presented herein. This AI-based correlation gave a promising level of accuracy, with R-squared values of 92% and 90% for the training and blind-testing datasets, respectively. This correlation can serve as an accurate and easy-to-use tool for porosity prediction without any prior experience in utilizing or implementing machine learning models.

Unlike conventional gas reservoirs, fluid flow in shale gas reservoirs is characterized by complex interactions between various factors, such as stress sensitivity, matrix shrinkage, and critical desorption pressure. These factors play a crucial role in determining the behavior and productivity of shale gas reservoirs. Stress sensitivity refers to the stress changes caused by formation pressure decline during production, where the shale gas formation becomes more compressed and its porosity decreases. Matrix shrinkage, on the other hand, refers to the deformation of the shale matrix due to the gas desorption process once the reservoir pressure reaches the critical desorption pressure where absorbed gas molecules start to leave the matrix surface, causing an increase in shale matrix porosity. Therefore, the accurate estimation of gas reserves requires careful consideration of such unique and complex interactions of shale gas flow behavior when using a material balance equation (MBE). However, the existing MBEs either neglect some of these important parameters in shale gas reserve analysis or employ an iterative approach to incorporate them. Accordingly, this study introduces a straightforward modification to the material balance equation. This modification will enable more accurate estimation of shale gas reserves by considering stress sensitivity and variations in porosity during shale gas production and will also account for the effect of critical desorption pressure, water production, and water influx. By establishing a linear relationship between reservoir expansion and production terms, we eliminate the need for complex and iterative calculations. As a result, this approach offers a simpler yet effective means of estimating shale gas reserves without compromising accuracy. The proposed MBE was validated using an in-house finite element poro-elastic model which accounts for stress re-distribution and deformation effects during shale gas production. Moreover, the proposed MBE was tested using real-field data of a shale gas reservoir obtained from the literature. The results of this study demonstrate the reliability and usefulness of the modified MBE as a tool for accurately assessing free and adsorbed shale gas volumes.

The Gulf Cooperation Council (GCC) region has witnessed significant growth in the global electrical and electronic equipment (EEE) market, especially in the industrial field due to the high demand from oil and gas and other related sectors. However, the lifespan of these end-of-life (EoL) products has become shorter, leading to electronic failure and generating electronic waste (e-waste). Disposing of such waste in recycling centers and landfills poses a challenge for policymakers and waste management officials due to its environmental impact. However, it is imperative to implement new management practices to overcome e-waste from landfills; thus, we propose the remanufacturing process as a viable and economic strategy for e-waste management. The process of industrial remanufacturing has the potential to decrease e-waste and promote the reuse of obsolete EEE and industrial devices., including those used in the Oil and Gas sector. This paper advocates for industrial remanufacturing as a solution to e-waste, aiming to increase the reusability of EoL EEE products. The authors provide a detailed analysis of the troubleshooting process and the tools employed, emphasizing the requirements for adopting this crucial remanufacturing solution. Moreover, the benefits of remanufacturing to industries and stakeholders are highlighted by offering a cost-effective alternative to replacing equipment, increasing the reusability of obsolete products, and reducing e-waste. By addressing the challenges of adopting remanufacturing, limitations and areas for future focus to enhance sustainability can be identified. Additionally, a comprehensive technical survey of the most common reasons for electronic failure at the board level demonstrates the feasibility and practicality of remanufacturing processes. These valuable insights reveal the possibility of realizing remanufacturing and guide technicians and stakeholders in implementing remanufacturing practices in various sectors, including oil and gas, petrochemicals, power generation, and factories. Lastly, by showcasing an example of a GCC region facility specializing in remanufacturing industrial electronic equipment, the potential contribution to a more sustainable future is emphasized,; this makes it easier to advocate for the adoption of remanufacturing as a more sustainable and economically viable approach in the industrial sector, particularly in oil and gas, for effective e-waste management.

Horizontal wells are a proven and well acknowledged technology to enhance well productivity through an increase in reservoir contact compared to that of a vertical well under the same conditions. In the last three decades, a considerable effort has been directed to study flow around horizontal wells by many investigators. These studies have mainly focused on proposing practical tools (in the form of skin factor) for long-term well productivity estimation. The skin factor proposed can be applied in an equivalent (one dimensional radial) open-hole system replicating the flow around the actual complex three dimensional (3-D) flow geometry of the Horizontal well. However, all these studies concentrate on single-phase Darcy flow conditions.In gas condensate reservoirs, in addition to the three dimensional (3-D) nature of flow geometry, the flow behavior is further complicated by the phase change and the variation of relative permeability (k r ) due to the coupling (increase in k r by an increase in velocity or decrease in IFT) and inertia (a decrease in k r by an increase in velocity) effects. Therefore, simulating such a complex 3-D flow using numerical commercial simulators requires a three dimensional fine grid compositional approach, which is very impractical, cumbersome and sometimes trigger convergence problems due to numerical instability. In fact, the introduction of a quick and reliable tool for long term productivity calculation is much needed in such systems.This work is aimed at the development of a practical, general, and easy-to-use method for defining an effective wellbore radius of an equivalent open-hole system, replicating flow around the 3-D Horizontal well in gas condensate reservoirs. Accordingly, a 3-D compositional finite element based in-house simulator was developed to accurately model gas and gas condensate flow around horizontal wells. A large data bank was generated, covering the impact of a wide range of pertinent geometric and flow parameters on the well performance. Then a general approach is proposed for estimation of an effective wellbore radius of an equivalent open-hole radial 1-D system replicating flow around the 3-D Horizontal well system. The effective wellbore radius varies with fluid properties, velocity, IFT, reservoir and wellbore conditions. The results of the proposed formulation, which benefits from suitable dimensionless numbers, has been tested against the simulator results not used in its development confirming the integrity of the approach. Also, the proposed formulation is applicable for both single-phase non-Darcy and two-phase gas condensate flow systems.With this approach, no numerical simulation is needed and instead a simple excel spread-sheet can predict the horizontal well performance, significantly facilitating engineering and management decisions relating to the application of horizontal well technologies.