Experience in the oil industry has shown that it is challenging to sustain successful long-term matrix injection, as injection water quality cannot be maintained rigorously due to facility hiccups and membrane clogging. Most oil field operators have resolved this problem of injectivity decline by increasing the surface injection pressure to part the formation and inject just above the fracture gradient with strict offtake management for zonal conformance. This is not an easy task as injection much above fracture opening pressure can lead to water fingering and poor sweep that results in uneconomical waterflood recovery. The operators, thus, strive to inject at a pressure just above the fracture opening pressure so that the fracture opens near the wellbore but does not extend and then maintain the pressure just above the fracture closing pressure. Therefore, determination of the fracture opening pressure and fracture closing pressure has remained critical data for the success of waterflood projects. The most reliable industry approach to estimating fracture opening and fracture closing pressures comes from the step rate test (SRT). This traditional approach of Cartesian analysis of pressure-rate plot fits straight lines through the data in a plot of injection pressure against injection rate and then estimates the fracture pressure from the intersection of these lines having different slopes. The data received often do not exhibit one clear change in slope, thus resulting in multiple possible solutions, making it difficult for the operator to use the data for high CAPEX facilities design. Most past studies indicate the subjectivity of this Cartesian slope fitting technique. Alternative solutions through multirate superposition analysis found limited application in the analysis of SRT data due to considerable sensitivity to the value of initial pressure used for superposition and lack of stability of rate and pressure data. In this article, a new technique of SRT analysis is presented, which provides a unique solution for fracture opening and fracture closing pressures. It helps to overcome the limitations of the traditional technique of arbitrary fitting of straight lines. It uses the mathematical understanding of cumulative derivatives to recognize that the matrix opens when the cumulative growth of the rate of injectivity shows a change. It estimates the derivative of the injection rate with respect to injection pressure at each step. Then, it estimates the fracture pressure from the plot of the cumulative of this derivative against pressure at each step. It helps to overcome the challenge SRT solutions posed by the nonlinear trend of pressure data at each injection step both before fracture and after fracture is initiated. It also overcomes the limitations of the multirate superposition technique, as it is not sensitive to the value of initial pressure used for superposition.
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.