Treatment of the filter cake layer after drilling is essential for better cement integrity and to retain the original reservoir permeability. Compared to water-based filter cake, oil-based mud filter cake removal is more sophisticated as oil encloses the filter cake’s particles. Therefore, oil-based mud clean-up requires wettability alteration additives (mutual solvents and/or surfactants) for permitting acid/filter cake reaction. With an appropriate acid, microemulsions were reported to be very efficient in cleaning oil-based filter cakes, due to their low interfacial tension and high acid solubility. The objective of this paper is to provide an overview of the different techniques and treatment solutions utilized in oil-based filter cake clean-up. Furthermore, a synopsis of the various treatments for drilling fluids densified with different weighting materials is presented. Subsequently, the research limitations and opportunities have been highlighted for future work. In the light of the review that has been presented in this paper, it's recommended to conduct further investigation on some areas related to filter cake removal. The removal of filter cake formed from weighting materials other than barite, calcium carbonate, ilmenite, and manganese tetroxide needs to be investigated thoroughly. Additionally, the overall efficiency of oil-based mud removal needs to be studied under wide ranges of temperature, salinity, and pH. The utilization of surfactant-free microemulsions in filter cake treatment could also be investigated.
Due to high oil and gas production and consumption, unconventional reservoirs attracted significant interest. Total organic carbon (TOC) is a significant measure of the quality of unconventional resources. Conventionally, TOC is measured experimentally; however, continuous information about TOC is hard to obtain due to the samples’ limitations, while the developed empirical correlations for TOC were found to have modest accuracy when applied in different datasets. In this paper, data from Devonian Duvernay shale were used to develop an optimized empirical correlation to predict TOC based on an artificial neural network (ANN). Three wells’ datasets were used to build and validate the model containing over 1250 data points, and each data point includes values for TOC, density, porosity, resistivity, gamma ray and sonic transient time, and spectral gamma ray. The three datasets were used separately for training, testing, and validation. The results of the developed correlation were compared with three available models. A sensitivity and optimization test was performed to reach the best model in terms of average absolute percentage error (AAPE) and correlation coefficient (R) between the actual and predicted TOC. The new correlation yielded an excellent match with the actual TOC values with R values above 0.93 and AAPE values lower than 14%. In the validation dataset, the correlation outperformed the other empirical correlations and resulted in less than 10% AAPE, in comparison with over 20% AAPE in other models. These results imply the applicability of this correlation; therefore, all the correlation’s parameters are reported to allow its use on different datasets.
Rock elastic properties such as Poisson’s ratio influence wellbore stability, in-situ stresses estimation, drilling performance, and hydraulic fracturing design. Conventionally, Poisson’s ratio estimation requires either laboratory experiments or derived from sonic logs, the main concerns of these methods are the data and samples availability, costs, and time-consumption. In this paper, an alternative real-time technique utilizing drilling parameters and machine learning was presented. The main added value of this approach is that the drilling parameters are more likely to be available and could be collected in real-time during drilling operation without additional cost. These parameters include weight on bit, penetration rate, pump rate, standpipe pressure, and torque. Two machine learning algorithms were used, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). To train and test the models, 2905 data points from one well were used, while 2912 data points from a different well were used for model validation. The lithology of both wells contains carbonate, sandstone, and shale. Optimization on different tuning parameters in the algorithm was conducted to ensure the best prediction was achieved. A good match between the actual and predicted Poisson’s ratio was achieved in both methods with correlation coefficients between 0.98 and 0.99 using ANN and between 0.97 and 0.98 using ANFIS. The average absolute percentage error values were between 1 and 2% in ANN predictions and around 2% when ANFIS was used. Based on these results, the employment of drilling data and machine learning is a strong tool for real-time prediction of geomechanical properties without additional cost.
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