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. Several ISC projects have been carried out in Venezuela in Tia Juana, Morichal, Miga, and Melones fields. Although the technical results have been satisfactory in terms of viscosity reduction and improved crude oil properties (such as °API), other important aspects of project evaluations have not been convincing due to the following factors: high temperatures in producing wells, acid gases management, generation of complex emulsions, corrosion, and high CAPEX and OPEX costs. Nevertheless, additional research work has been conducted on process optimization, using catalysts and hydrogen donors, to better address these other factors. Due to the great need to increase hydrocarbon production in Venezuela and to the advantages of ISC as an upgrading technique where low-carbon fuels and hydrogen as byproducts are generated, this paper presents a revisit of ISC projects in Venezuela from R&D technical aspects to field applications. It seeks to identify the main insights regarding the success and failure of the evaluated projects and make substantiated recommendations in the case of future applications of this technology.
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 R2 (0.9915 and 0.9926, for APG and AEC respectively). It was indicated that both monolayer and multilayer adsorption took place in a heterogeneous adsorption system with non-uniform surfactant molecules distribution, which was in remarkable agreement with the simulation results. The (APG/AEC) binary mixture prediction depicted contradictory results to the experimental individual behaviour, showing that AEC had more affinity to adsorb in competition with APG for the adsorption sites on the rock surface. The adopted ANN model showed good agreement with the experimental data and the simulated adsorption values for APG and AEC showed a decreasing trend as temperature increases. Simulating the impact of binary surfactant adsorption can provide a tremendous advantage of demonstrating the binary system behaviour with less experimental data. The utilization of ANN for such prediction procedure can minimize the experimental time, operating cost and give feasible predictions compared to other computational methods. The integrated workflow followed in this study is quite innovative as it has not been employed before for surfactant adsorption studies.