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Asphaltene precipitation and deposition have been a formation damage problem for decades, with the most devastating effects being wettability alteration and permeability impairment. To this effect, a critical look into the laboratory studies and models developed to quantify/predict permeability and wettability alterations are reviewed, stating their assumptions and limitations. For wettability alterations, the mechanism is predominantly surface adsorption, which is controlled by the asphaltene contacting minerals as they control the surface chemistry, charge, and electrochemical interactions. The most promising wettability alteration evaluation techniques are nuclear magnetic resonance, ζ potential, and the use of high-resolution microscopy. The integration of such techniques, which is still missing, would reinforce the understanding of asphaltene interaction with rock minerals (especially clays), which holds the key to developing a strategy for modeling wettability alteration. With regard to permeability impairment, surface deposition, pore plugging, and fine migration have been identified as the dominant mechanisms with several models reporting the simultaneous existence of multiple mechanisms. Existing experimental findings showed that asphaltene deposition is non-uniform due to mineral distribution which further complicates the modeling process. It also remains a challenge to separate changes due to adsorption (wettability changes) from those due to pore size reduction (permeability impairment).

This review presents the latest update, applications, techniques of the NMR tools in both laboratory and field scales in the oil and gas upstream industry. The applications of NMR in the laboratory scale were thoroughly reviewed and summarized such as porosity, pores size distribution, permeability, saturations, capillary pressure, and wettability. NMR is an emerging tool to evaluate the improved oil recovery techniques, and it was found to be better than the current techniques used for screening, evaluation, and assessment. For example, NMR can define the recovery of oil/gas from the different pore systems in the rocks compared to other macroscopic techniques that only assess the bulk recovery. This manuscript included different applications for the NMR in enhanced oil recovery research. Also, NMR can be used to evaluate the damage potential of drilling, completion, and production fluids laboratory and field scales. Currently, NMR is used to evaluate the emulsion droplet size and its behavior in the pore space in different applications such as enhanced oil recovery, drilling, completion, etc. NMR tools in the laboratory and field scales can be used to assess the unconventional gas resources and NMR showed a very good potential for exploration and production advancement in unconventional gas fields compared to other tools. Field applications of NMR during exploration and drilling such as logging while drilling, geosteering, etc., were reviewed as well. Finally, the future and potential research directions of NMR tools were introduced which include the application of multi-dimensional NMR and the enhancement of the signal-to-noise ratio of the collected data during the logging while drilling operations.

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.

In this work, a new method to evaluate the reaction kinetics of different stimulation fluids with carbonate rocks was introduced. NMR diffusion measurements were used to determine the acid diffusion coefficient and the acid tortuous path inside carbonate rocks. Reaction kinetics can also be evaluated using rotating disk apparatus (RDA) in which a disc is rotated in the bulk fluid at different rotational speeds. RDA does not represent the actual, restricted acid diffusion that takes place in the porous media because only the surface of the rock is exposed to the reaction and the acid is not confined in the porous media. NMR diffusion measurements can accurately describe and determine the acid restricted diffusion in porous media. The diffusion coefficient of the acid is a crucial term in describing the reaction kinetics of acids with carbonate rocks. It is also used to predict the optimum injection rate required during the acidizing treatment and the soaking time for different fluids required to remove scales and deposits in the wellbore. The restricted diffusion was determined for different fluids such as GLDA chelating agent, HEDTA chelating agent, and EDTA chelating agent. Core flooding experiments for each fluid were conducted to determine the optimum injection rate. NMR restricted diffusion measurements then were conducted to determine the restricted diffusion and in turn to determine the optimum injection rate. The optimum injection rate estimated from the NMR was compared to that from the core flooding experiments. The results were a good match showing that NMR is a suitable, reliable, and robust method to evaluate reaction kinetics of different fluids with carbonate rocks.

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Binary soil mixture, containing large silica particles (sand) mixed with variable content of very fine silt or clay, is an example of a functionally graded material that is important for several science and engineering applications. Predicting the porosity (or void ratio), which is a fundamental quantity that affects other physical properties, of such material as function of fines (clay or silt) fraction can be significant for sediment research and material design optimization. Existing analytical models for porosity prediction work well for binary mixed soils containing multi-sized non-cohesive particles with no clay, while such models frequently underestimate the porosity of sand-clay mixtures. This study aims to present an analytical model that accurately predicts the porosity of mixed granular materials or soils containing sand and very fine silt or clay (cohesive particles). It is demonstrated that accounting for the cohesive nature of very fine particles, which exists due to the effect of inter-particle forces, is a major missing aspect in existing packing models for mixed soils. Consequently, a previously developed linear packing model is modified so that it accounts for fines cohesive packing in sand-fines mixtures. The model prediction is validated using various experimental published data sets for the porosity of sand-fines mixtures. Improvement in the prediction of permeability and maximum packing dry density when incorporating cohesive packing behavior is discussed. The current model also provides important insights on the conditions under which, the lowest permeability and maximum packing state are expected.