Wettability is a challenging property of oil and gas shale reservoirs, which are geologically and geochemically intricate formations. Furthermore, the mineral composition, organic matter content, generated pore types, pressure, temperature, and saturation conditions significantly affect the observed wetting affinity. Hence, a deep understanding of the complexities facing the researchers is needed. This review analyzes the most relevant published research on the wettability evaluation of shales for a better understanding and delineation. Here, the effects of shale composition, organic matter content, maturity, pore types, and reservoir conditions are addressed. It also focuses on the unconventional and emerging approaches to the wettability evaluation of shales. At last, future perspectives, concluding remarks, and recommendations are presented.

The non-biodegradable additives used in controlling drilling fluid properties cause harm to the environment and personal safety. Thus, there is a need for alternative drilling fluid additives to reduce the amount of non-biodegradable waste disposed to the environment. This work investigates the potential of using mandarin peels powder (MPP), a food waste product, as a new environmentally friendly drilling fluid additive. A complete set of tests were conducted to recognize the impact of MPP on the drilling fluid properties. The results of MPP were compared to low viscosity polyanionic cellulose (PAC-LV), commonly used chemical additive for the drilling fluid. The results showed that MPP reduced the alkalinity by 20-32% and modified the rheological properties (plastic viscosity, yield point, and gel strength) of the drilling fluid. The fluid loss decreased by 44-68% at concentrations of MPP as less as 1-4%, and filter cake was enhanced as well when comparing to the reference mud. In addition, MPP had a negligible to minor impact on mud weight, and this effect was resulted due to foaming issues. Other properties such as salinity, calcium content, and resistivity were negligibly affected by MPP. This makes MPP an effective material to be used as pH reducer, a viscosity modifier, and an excellent fluid loss agent. This work also provides a practical guide for minimizing the cost of the drilling fluid through economic, environmental, and safety considerations, by comparing MPP with PAC-LV.

Serious problems will be presented due to using conventional chemical additives to regulate the drilling mud properties, as they have health, safety, and environmental side effects. Thus, there is a considerable necessity for alternative multifunctional bio-enhancer drilling mud additives, which can assist in optimizing the drilling fluid specifications and enhance its effectiveness with the least effects on the environment and the drilling personnel safety. The effects of adding two concentrations of palm tree leaves powder (PTLP) to water-based mud were conducted under fresh and aged conditions using standard API drilling fluids testing methods such as rheometer/viscometer, pH meter and temperature, and filter press. All tests results were minutely recorded to understand the influence of PTLP additives on the drilling mud properties. The results indicated that PTLP as an effective material to be used as pH reducer, viscosity reducer, and as an excellent filtration loss control agent under the surface and sub-surface conditions. Thus, PTLP has excellent feasibility to be utilized as biodegradable drilling mud additive replacing or at least supporting other conventional chemical additives, which have usually been used for the same purposes such as lignosulphonate, chrome-lignite, and Resinex. Finally, this work can serve as a practical guide for minimizing the cost of the drilling fluid and reducing the amount of non-biodegradable waste disposed to the environment.

Lost circulation is a serious problem that imposes some extra costs to petroleum and gas exploration operations. Substantial technical and economic benefits can be accomplished if the severity and frequency of mud loss are considered during the well planning procedure. This will lead to preventing the occurrence of losses by using treatments/solutions that are applied before entering lost circulation zones. In the present work, new models were developed to predict the amount of lost circulation using artificial neural networks (ANNs). This model was implemented to obtain a deeper understanding of the relations between the losses rate and the controllable drilling variables (i.e., rate of penetration [ROP], flow rate [FR], circulation pressure [CP], weight on bit [WOB], and rotation per minute [RPM]). The losses rate was found to be sensitive to high ROP, FR, and CP, such that increasing these parameters continuously increase the amount of lost circulation. While a slight rise in the losses rate was observed at high WOB and RPM. The proposed ANNs model was used to predict the losses rate for two wells, and comparison plot (actual amount of lost circulation versus predicted) was introduced as a function of depth. An accurate and early prediction of lost circulation has been of great importance to avoid the risks associated with this problem's occurrence.

Equivalent circulation density (ECD) is vital in drilling operations. Poor management of ECD can lead to many drilling obstacles that can increase the delivery time of the well. The aim of this study is to utilize artificial neural networks (ANNs) to create a model to estimate ECD prior to drilling. Data of more than 2000 wells collected from multiple sources worldwide were utilized in this work. An ANN model was created with one hidden layer and 12 neurons in the hidden layer. The data were clustered into three data sets; training (70% of the data), verification (15% of the data), and testing (15% of the data). Ten training algorithms were utilized to train the network, the training algorithm with the lowest mean squared error (MSE) and the highest R2 was selected to achieve the best predictive model. Bayesian Regularization (BR) algorithm was selected to train the model because it had the highest R2 and the lowest MSE. The results showed that the created model can predict ECD within an acceptable margin of error. The overall R2 of the model was 0.982 which is considered very good. Alternatively, given a target of ECD, the created model can be utilized in reverse to obtain the desired ECD by altering the key drilling parameters affecting the ECD model (the inputs). This will help the drilling personnel to optimize ECD in the field. Intelligent systems and machine learning have proven their effectiveness in solving complicated problems that cannot be solved analytically. With the large historical drilling data available in the oil and gas industry, machine learning and intelligent systems can be used to make better future decisions that will help to optimize the drilling operations.