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Elastic Moduli contrast between the adjacent layers in a layered formation can lead to various problems in a conventional hydraulic fracturing job such as improper fracture height growth, limited penetration in a weaker layer only, and non-conductive fractures. In this study, a result of thermochemical fracturing experiment is presented. The hydraulic fracturing experiments presented in this study were carried out on four layered very tight cement block samples. The results revealed that the novel fracturing technique can lower the required breakdown pressure in a layered rock by 26%, from 1495 psi (reference breakdown pressure recorded from conventional hydraulic fracturing technique) to 1107 psi (breakdown pressure recorded in thermochemical fracturing). The post treatment experimental analysis showed that the thermochemical fracturing approach resulted in deep and long fractures, passing through majority of the layers, while conventional hydraulic fracturing resulted in a thin fracture affected only the top layer. A productivity analysis was also carried out which suggested that the fracturing with thermochemical fluids can raise the oil flow rate up to 76% when compared to a conventional hydraulic fracturing technique. Thermochemical fluids injection caused the creation of microfractures and reduces the linear elastic parameters of the rocks. The new technique is cost effective, non-toxic, and sustainable in terms of no environmental hazards.

Carbonate rocks present a complicated pore system owing to the existence of intra-particle and interparticle porosities. Therefore, characterization of carbonate rocks using petrophysical data is a challenging task. Conventional neutron, sonic, and neutron-density porosities are proven to be less accurate as compared to the NMR porosity. This study aims to predict the NMR porosity by implementing three different machine learning (ML) algorithms using conventional well logs including neutron-porosity, sonic, resistivity, gamma ray, and photoelectric factor. Data, comprising 3500 data points, was acquired from a vast carbonate petroleum reservoir in the Middle East. The input parameters were selected based on their relative importance with respect to output parameter. Three ML techniques such as adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and functional network (FN) were implemented for the development of prediction models. The model’s accuracy was evaluated by correlation coefficient (R), root mean square error (RMSE), and average absolute percentage error (AAPE). The results demonstrated that all three prediction models are reliable and consistent exhibiting low errors and high ‘R’ values for both training and testing prediction when related to actual dataset. However, the performance of ANN model was better as compared to other two studied ML techniques based on minimum AAPE and RMSE errors (5.12 and 0.39) and highest R (0.95) for testing and validation outcome. The AAPE and RMSE for the testing and validation results were found to be 5.38 and 0.41 for ANFIS and 6.06 and 0.48 for FN model, respectively. The ANFIS and FN models exhibited ‘R’ 0.937 and 0.942, for testing and validation dataset, respectively. Based on testing and validation results, ANFIS and FN models have been ranked second and third after ANN. Further, optimized ANN and FN models were used to extract explicit correlations to compute the NMR porosity. Hence, this study reveals the successful applications of ML techniques for the accurate prediction of NMR porosity.

Enzyme-induced calcium carbonate precipitation (EICP) techniques are used in several disciplines and for a wide range of applications. In the oil and gas industry, EICP is a relatively new technique and is actively used for enhanced oil recovery applications, removal of undesired chemicals and generating desired chemicals in situ, and plugging of fractures, lost circulation, and sand consolidation. Many oil- and gas-bearing formations encounter the problem of the flow of sand grains into the wellbore along with the reservoir fluids. This study offers a detailed review of sand consolidation using EICP to solve and prevent sand production issues in oil and gas wells. Interest in bio-cementation techniques has gained a sharp increase recently due to their sustainable and environmentally friendly nature. An overview of the factors affecting the EICP technique is discussed with an emphasis on the in situ reactions, leading to sand consolidation. Furthermore, this study provides a guideline to assess sand consolidation performance and the applicability of EICP to mitigate sand production issues in oil and gas wells.

AVO and seismic inversion has been successfully applied during the exploration phase of the Late Messinian Abu Madi Formation in the central Nile Delta of Egypt. Several gas condensate discoveries were made using this technique as a Direct Hydrocarbon Indicator (DHI) and risk mitigation tool. Deterministic inversion was further employed to delineate the overall lateral extent of the field and gross reservoir character during the appraisal phase of one particular field. A comprehensive well data acquisition programme for several wells revealed a complex lateral and vertical reservoir stacking patterns with highly heterogeneous reservoir parameters. The reservoir sequence is attributed to lacustrine turbidities deposited in semi-isolated Late Messinian Basins. Informally, the Abu Madi Formation can be subdivided in to upper and lower members. Intraformational shale barriers and baffles are commonplace. The stacked reservoir sands cannot be visualised and differentiated by the existing deterministic inversion products but each sand package displays complex vertical and lateral pressure trends. This paper describes an AVA Geostatistical Inversion process integrated with rock physics modelling (using differential effective medium theorem) of the wells to generate high resolution multiple rock property models with the aim of capturing reservoir heterogeneity and the observed pressure trends. The probabilistic representations of lithology, water saturation, permeability and effective porosity captured the range of uncertainty remaining after appraisal drilling to provide equi-plausible models to further de-risk future development of the field. Flow simulation modelling is used to monitor and predict pressure depletion trends to optimise further infill drilling and effectively manage the reservoir. Introduction The Late Messinian Abu Madi Formation is a prolific producing reservoir in the Nile Delta of Egypt. The first discoveries were made from the late 1960's onwards focused along the Abu Madi Valley trend (Rizk, 2002). A number of fields were subsequently discovered within this north-south trending incised valley trend. More recently exploration focused to the west of the Abu Madi Valley trend in the central Nile Delta area. Despite near-continuous exploration from the mid-1960's the area yielded no commercial discoveries. Following a phase of extensive 3D seismic acquisition and interpretation several fields were discovered from 2007 onwards. Most discoveries have made on structurally robust traps which usually comprises some stratigraphic trapping elements (facies changes and sand pinch-out). All discoveries were supported by positive AVO responses which generally conform to the mapped structural traps.

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