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This study offered a detailed review of data sciences and machine learning (ML) roles in different petroleum engineering and geosciences segments such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, emphasizing the newly emerging field of unconventional reservoirs. The future of data science and ML in the oil and gas industry, highlighting what is required from ML for better prediction, is also discussed. This study also provides a comprehensive comparison of different ML techniques used in the oil and gas industry. With the arrival of powerful computers, advanced ML algorithms, and extensive data generation from different industry tools, we see a bright future in developing solutions to the complex problems in the oil and gas industry that were previously beyond the grip of analytical solutions or numerical simulation. ML tools can incorporate every detail in the log data and every information connected to the target data. Despite their limitations, they are not constrained by limiting assumptions of analytical solutions or by particular data and/or power processing requirements of numerical simulators. This detailed and comprehensive study can serve as an exclusive reference for ML applications in the industry. Based on the review conducted, it was found that ML techniques offer a great potential in solving problems in almost all areas of the oil and gas industry involving prediction, classification, and clustering. With the generation of huge data in everyday oil and gas industry activates, machine learning and big data handling techniques are becoming a necessity toward a more efficient industry.

Acid fracturing is applied to increase the productivity of carbonate formations. The acid creates rough fracture surfaces and channels that keep the fractures open after closure. This study investigated a chelating agent and HCl acid as acid fracturing fluids in three different carbonates: Austin chalk, Indiana limestone, and Silurian dolomite. The impact of rock hardness and surface roughness on conductivity was thoroughly studied. We collected six core samples, two from each type, with 1.5-in. × 6-in. dimensions. A saw was used to cut the samples, creating smooth initial fracture surfaces. A Brinell hardness tester (FH-9 model) was used to measure the rock strength, while a Kruss high-resolution surface analyzer (SRA) was used to scan the rock surfaces and measure the roughness. The acid fracturing treatment was carried out using a coreflooding system at 100 °C and 1000 psi confining pressure. The fracture conductivity was measured before and after treatment utilizing different flow rates (2.5–10 cc/min) and overburden pressures (1000–2500 psi). The reactivity of acid with a rock was quantified by tracing the calcium ions in the effluents collected from coreflooding outlet at a fixed time interval. The analysis was conducted with inductively coupled plasma (ICP). It is observed that rock hardness and roughness have a significant impact on fracture conductivity. Also, the type of treating fluid and rock determines the generated rock roughness, where higher reactivity results in higher roughness and hence conductivity. Glutamic diacetic acid (GLDA) chelating agent generated sufficient fracture conductivity in chalk and limestone rocks, while HCl acid generated high conductivity in the three rock types. This study sheds new insights on the selection of the acid fracturing fluid for different carbonates, and less reactive rocks such as dolomites are not good candidates to be treated with chelating agents because of its low reactivity. Calcite rocks (i.e., limestone and chalk) can be treated with chelating agents or strong acids.

Unconfined compressive strength (UCS) is the key parameter to; estimate the insitu stresses of the rock, design optimal hydraulic fracture geometry and avoid drilling problems like wellbore instability. UCS can be estimated by rock mechanical tests on core plugs retrieved from the depth of interest but retrieving reservoir rock samples throughout the depth of the reservoir section and performing laboratory tests on them are extremely expensive as well as time consuming. In absence of core plugs, UCS can be estimated from empirical correlations. Most of the empirical correlations for UCS prediction reported in the literature are based on elastic parameters or on compressional wave velocity. These correlations were developed using linear or non-linear regression techniques. Artificial intelligence tools once optimized for training can successfully model UCS since these tools can capture highly complex and non-linear relationship between input parameters and the output parameter. The objective of this research study is to accurately predict UCS of rock using basic geophysical well logs namely; bulk density, compressional, and shear wave velocities, by applying different artificial intelligence techniques namely; Support Vector Machine (SVM), Adaptive neuro fuzzy inference system (ANFIS) and Artificial neural network (ANN). The data set used in this study, comprised of 200 laboratory measured UCS values on core plugs and their corresponding well logs. The data were collected from 10 wells which were located in a giant carbonate reservoir. Based on minimum average absolute percentage error (AAPE) and highest coefficient of determination (R2) between actual and predicted data, ANN model proposed as the best model to predict UCS. A rigorous empirical correlation was developed using the weights and biases of ANN model to predict without the need of any software incorporating AI. A comparison of proposed model with other correlations to predict UCS on new data set also suggested that the proposed model gives less AAPE. Therefore, the proposed model seems very promising and can serve as a handy tool to help geo-mechanical engineers to determine the UCS of the carbonate rock.