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.

Understanding the depositional setting of siliciclastics reservoir is fundamental process to exploration and development of hydrocarbon reservoirs and to the multi-phase cycle of the oil and gas industry. Typically, core samples from existing or potential reservoirs can be used for interpretation of depositional environment. However, the lack of core samples in certain reservoirs represents a challenge for reservoir development plans and further exploration. To overcome the absence of core samples in the middle Miocene Sidri Member in the Badri field, Gulf of Suez, this study aimed to reconstruct its depositional settings by coupling well logging patterns and petrographic characterization of ditch cuttings. Consequently, 30 thin section samples representing the reservoir section of four wells were described in detail and standard petrographic characteristics were determined. Then, gamma-ray (GR) log patterns of the studied reservoir wells were used for interpretation of depositional environment. Petrographic analysis indicates that the sandstone reservoir is fine- to medium-grained arkose with dominant siliceous cement and composed mainly of quartz, feldspars, and lithic fragments. Pores reflecting primary and secondary porosity as well as inter-granular pores are dominant. The facies analysis indicates that the studied member has a heterogeneous nature and rapid facies change; its main lithofacies are blocky sandstones, intercalated sandstones and shales, and shales. Three electrofacies were interpreted in the studied section: (1) electrofacies-A (blocky sandstones), which is characterized by a cylindrical-shaped (right boxcar) GR trend; (2) electrofacies-B (intercalated sandstones and shales), which is characterized by an irregular log trend with serrated shape; and (3) electrofacies-C (shales), which is characterized by irregular GR trend and has no character. The interpreted results indicate a tidal channel depositional setting for electrofacies-A, mixed tidal flat depositional setting for electrofacies-B, and low relief offshore mudstone depositional setting for electrofacies-C. Finally, the results illustrate how the coupling of GR patterns with the analysis of petrographic characteristics can be used to understand the depositional setting of a hydrocarbon reservoir that lacks core samples. This work could be useful for assessment of reservoir distribution and quality, for reduction of uncertainty during field development, as well as for providing useful insight to similar hydrocarbon reservoirs elsewhere.

Shear wave velocity (V_S) data from sedimentary rock sequences is a prerequisite for implementing most mathematical models of petroleum engineering geomechanics. Extracting such data by analyzing finite reservoir rock cores is very costly and limited. The high cost of sonic dipole advanced wellbore logging service and its implementation in a few wells of a field has placed many limitations on geomechanical modeling. On the other hand, shear wave velocity V_S tends to be nonlinearly related to many of its influencing variables, making empirical correlations unreliable for its prediction. Hybrid machine learning (HML) algorithms are well suited to improving predictions of such variables. Recent advances in deep learning (DL) algorithms suggest that they too should be useful for predicting V_S for large gas and oil field datasets but this has yet to be verified. In this study, 6622 data records from two wells in the giant Iranian Marun oil field (MN#163 and MN#225) are used to train HML and DL algorithms. 2072 independent data records from another well (MN#179) are used to verify the V_S prediction performance based on eight well-log-derived influencing variables. Input variables are standard full-set recorded parameters in conventional oil and gas well logging data available in most older wells. DL predicts V_S for the supervised validation subset with a root mean squared error (RMSE) of 0.055 km/s and coefficient of determination (R²) of 0.9729. It achieves similar prediction accuracy when applied to an unseen dataset. By comparing the V_S prediction performance results, it is apparent that the DL convolutional neural network model slightly outperforms the HML algorithms tested. Both DL and HLM models substantially outperform five commonly used empirical relationships for calculating V_S from V_p relationships when applied to the Marun Field dataset. Concerns regarding the model's integrity and reproducibility were also addressed by evaluating it on data from another well in the field. The findings of this study can lead to the development of knowledge of production patterns and sustainability of oil reservoirs and the prevention of enormous damage related to geomechanics through a better understanding of wellbore instability and casing collapse problems. Graphical