Explicit Data-Based Model for Predicting Oil-Based Mud Viscosity at Downhole Conditions.

One of the functions of drilling mud is to transport cuttings out of a drilled well. To fulfill this purpose, the mud must possess optimal viscous characteristics. The main goal of this study is to develop a model for estimating the plastic viscosity (PV) of oil-based muds (OBMs) at downhole conditions, especially when high-temperature, high-pressure wells are drilled.

Workflow of the In Situ Combustion EOR Method in Venezuela: Challenges and Opportunities.

In situ combustion (ISC) is one of the oldest thermal enhanced oil recovery methods to have been applied in Venezuela to increase the production of highly viscous crude oils, with a first field application in 1959 in the Tia Juana Field-Lake Maracaibo Basin. This method, which is characterized by high energy efficiency, consists of injecting air into the reservoir where exothermic oxidation reactions initiate to increase the mobility of the oil. Compared to other thermal enhanced oil recovery methods such as steam injection, ISC has a lower environmental impact in terms of water and fuel consumption, and emission of gases as the produced gases can be reinjected or stored.

Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application.

Throughout the application of enhanced oil recovery (EOR), surfactant adsorption is considered the leading constraint on both the successful implementation and economic viability of the process. In this study, a comprehensive investigation on the adsorption behaviour of nonionic and anionic individual surfactants; namely, alkyl polyglucoside (APG) and alkyl ether carboxylate (AEC) was performed using static adsorption experiments, isotherm modelling using (Langmuir, Freundlich, Sips, and Temkin models), adsorption simulation using a state-of-the-art method, binary mixture prediction using the modified extended Langmuir (MEL) model, and artificial neural network (ANN) prediction. Static adsorption experiments revealed higher adsorption capacity of APG as compared to AEC, with sips being the most fitted model with R (0.