Well stimulation using hydrochloric acid (HCl) is a common practice in carbonate reservoirs to overcome formation damage in the near wellbore area. Using HCl for matrix acidizing has many limitations at high-temperature (HT) conditions, such as tubulars corrosion and face dissolution due to the fast reaction rate. Chelating agents, such as L-glutamic acid-N,N-diacetic acid (GLDA), are alternatives to HCl to overcome these problems. We studied the effect of diluting GLDA in seawater on the reaction kinetics with carbonate rocks under HT conditions at low pH (3.8). Results of the reaction of carbonate at 1000 psi and 150, 200, and 250 °F with GLDA prepared in both fresh and seawater, GLDA/DI and GLDA/SW, respectively, are presented. The reaction kinetics experiments were carried out in HT rotating disk apparatus (RDA) at rotational speeds ranging from 500 to 2000 revolutions per minute (RPM) at a fixed temperature. Indiana limestone and Austin chalk were used to studying the effect of rock facies on the reaction of GLDA with rock samples. In both GLDA/DI and GLDA/SW, the reaction regime of 20 wt% GLDA (3.8 pH) with Indiana limestone was mass transfer limited. The reaction rate and diffusion coefficient were highly dependent on the temperature. For Austin chalk, at 200 °F and 1000 psi the diffusion coefficient of GLDA/SW is an order of magnitude of its value with Indiana limestone using the same fluid. Diffusion coefficients were used to estimate the optimum injection rate for stimulating HT carbonate formation and compared with coreflooding results. The data presented in this paper will support the numerical simulation of the acid flow in carbonate reservoirs.

The drilling rate of penetration (ROP) is defined as the speed of drilling through rock under the bit. ROP is affected by different interconnected factors, which makes it very difficult to infer the mutual effect of each individual parameter. A robust ROP is required to understand the complexity of the drilling process. Therefore, an artificial neural network (ANN) is used to predict ROP and capture the effect of the changes in the drilling parameters. Field data (4525 points) from three vertical onshore wells drilled in the same formation using the same conventional bottom hole assembly were used to train, test, and validate the ANN model. Data from Well A (1528 points) were utilized to train and test the model with a 70/30 data ratio. Data from Well B and Well C were used to test the model. An empirical equation was derived based on the weights and biases of the optimized ANN model and compared with four ROP models using the data set of Well C. The developed ANN model accurately predicted the ROP with a correlation coefficient (R) of 0.94 and an average absolute percentage error (AAPE) of 8.6%. The developed ANN model outperformed four existing models with the lowest AAPE and highest R value.

High-bentonite mud (HBM) is a water-based drilling fluid characterized by its remarkable improvement in cutting removal and hole cleaning efficiency. Periodic monitoring of the rheological properties of HBM is mandatory for optimizing the drilling operation. The objective of this study is to develop new sets of correlations using artificial neural network (ANN) to predict the rheological parameters of HBM while drilling using the frequent measurements, every 15 to 20 min, of mud density (MD) and Marsh funnel viscosity (FV). The ANN models were developed using 200 field data points. The dataset was divided into 70:30 ratios for training and testing the ANN models respectively. The optimized ANN models showed a significant match between the predicted and the measured rheological properties with a high correlation coefficient (R) higher than 0.90 and a maximum average absolute percentage error (AAPE) of 6%. New empirical correlations were extracted from the ANN models to estimate plastic viscosity (PV), yield point (Y_P), and apparent viscosity (AV) directly without running the models for easier and practical application. The results obtained from AV empirical correlation outperformed the previously published correlations in terms of R and AAPE.