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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.

The global growing energy demand driving the industry attention towards unconventional oil/gas resources due to limited conventional resources. Huge reserves of unconventional makes them promising and draw the industry attention, however oil/gas is stored in micro to nano scale pores with poor connectivity. It is very essential however difficult to quantify the flow characteristics in porous media in unconventional reservoirs due to complex pore network, irregular geometry of pore throat and non-homogeneous pore size distribution. Various experimental techniques to determine quantitative and qualitative characteristics of pore systems have been studied including scanning electron microscopy (SEM), nuclear magnetic resonance (NMR), micro/nano computed tomography (XCT), and fluid invasion (mercury injection capillary pressure and gas adsorption/desorption). The comparison analysis of results has been carried out that exhibited the ability of these techniques to get the information about the pore size distribution and limitations for different pore sizes. Best and reliable technique for characterizing the pore structures in unconventional has been identified. SEM and FE-SEM are only able to provide the qualitative parameters for pore morphology, distribution and connectivity of pores. Three-dimensional image of pore structure and network could be studied through micro-CT scan images however, its high expense and huge processing time due to observation of small region at certain resolution make its use limited. Nitrogen adsorption is only able to study the micropores in tight sandstones but it destructive nature limits its usage. Pressure controlled mercury porosimeter technique is not able to determine the microporosity directly and determine the throat. It does not provide pore throat distribution. Application of high pressure may damage the pore structure. However, this mercury injection constant pressure rate can be applied for yielding both pore sizes and capillary pressure of pore throats. Both small throat and large pore body can be investigated through this technique, but this technique has limitation in maximum injection pressure. NMR is able to provide the qualitative and quantitative delineation of pore structures features such as pore throat distribution, sizes, and pore fluid saturation, total and effective porosity, and permeability directly if supplemented with other techniques. Detailed analyses of different analytical techniques resulted that none of the technique is able to fully characterize the pore structure of unconventional tight rocks. Combination of more than one technique is the best solution for complete description and accurate determination of pore structure characteristics.

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