Ahmad Alkouh

Naturally fractured reservoirs are characterized by their complex nature due to the existence of natural fractures and fissures within the rock formations. These fractures can significantly impact the flow of fluids within the reservoir, making it difficult to predict and manage production. Therefore, efficient production from such reservoirs requires a deep understanding of the flow behavior via the integration of various geological, geophysical, and engineering data. Additionally, advanced simulation models can be used to predict reservoir behavior under different production scenarios and aid in decision making and effective management. Accordingly, this study presents a robust mathematical two-phase fluid flow model (FRACSIM) for the simulation of the flow behavior of naturally fractured reservoirs in a 3D space. The mathematical model is based on the finite element technique and implemented using the FORTRAN language within a poro-elastic framework. Fractures are represented by triangle elements, while tetrahedral elements represent the matrix. To optimize computational time, short to medium-length fractures adopt the permeability tensor approach, while large fractures are discretized explicitly. The governing equations for poro-elasticity are discretized in both space and time using a standard Galerkin-based finite element approach. The stability of the saturation equation solution is ensured via the application of the Galerkin discretization method. The 3D fracture model has been verified against Eclipse 100, a commercial software, via a well-test case study of a fractured basement reservoir to ensure its effectiveness. Additionally, the FRACSIM software successfully simulated a laboratory glass bead drainage test for two intersected fractures and accurately captured the flow pattern and cumulative production results. Furthermore, a sensitivity study of water injection using an inverted five-spot technique was tested on FRACSIM to assess the productivity of drilled wells in complex fractured reservoirs. The results indicate that FRACSIM can accurately predict flow behavior and subsequently be utilized to evaluate production performance in naturally fractured reservoirs.

This study presents a comprehensive material balance equation (MBE) to estimate the reserve of shale gas reservoirs including free and adsorbed gas volume. The presented material balance equation takes into account the effect of stress change, matrix shrinkage, water volume production and influx, and critical desorption pressure. The material balance equation is converted into a linear relationship between the reservoir production and expansion parameters used during the derivation procedures that include rock-fluid properties and production history data. The proposed straight line reserve evaluation technique yields a slope of original free and absorbed gas in organic matrix, while the y-intercept yields the volume of original free gas in the in-organic matrix. A field case study of shale gas located in Australia is presented. Results show that the proposed MBE and the corresponding straight line reserve evaluation technique are rational and competent in estimating the free gas and adsorbed gas volumes accurately with error less than 6% compared to the numerical simulation model presented in this study using an in-house simulator based on finite element technique and FORTRAN language. Hence, the presented technique in this study can be used as a quick and easy to use tool to accurately estimate the free and adsorbed gas reserves and to improve the development of the production strategies of shale gas reservoirs.

The oil and gas (O&G) field is the most sought-after industry in the Gulf Cooperation Countries (GCCs) and holds significant importance in the region’s economy. Therefore, this sector requires various industrial electrical, and electronics equipment (EEE) products to perform multiple tasks throughout the upstream, downstream, and midstream segments. However, as these EEE products approach their end of life (EoL), the sector faces the challenge of managing failed units. As a result, replacing or recycling failed EEE products can contribute to the growing problem of electronic waste (e-waste), which can have severe environmental consequences. In addition, while some EEE products can be repaired or remanufactured with low reliability, many others cannot be fixed due to various technical reasons. This paper’s primary goal is to propose a circular economy strategy and sustainable practices that promote the longevity of industrial EoL electronic products in the O&G sector through remanufacturing. We introduced and implemented a new mathematical score, the Index Of Repairability (IOR), which aims to assess the ease of EEE repairability in the O&G sector and improve their lifespan and durability based on four criteria: design, spare parts availability, software access, and documentation. This novel mathematical metric leverages the analytic hierarchy process (AHP) and set theory. Additionally, original equipment manufacturers (OEMs) can adopt and benefit from this innovative IOR by incorporating eco-design principles and designing more easily repairable industrial products for technicians, thereby reducing the negative impact of e-waste, enhancing stakeholder satisfaction, and minimizing downtime. Furthermore, governmental organizations can implement regulations and incentives to advocate for and mandate the use of the IOR by OEMs, ensuring that the electronics industry prioritizes repairability, remanufacturing, and sustainability.

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.

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.