Surfactants are typically used in hydraulic fracturing applications to perform a single function, which results in multiple surfactants being used during operations. In this study, flow loop and coreflood tests were conducted with slickwater fracturing fluid systems and analyzed in conjunction to observe the effectiveness of flowback surfactants and their ability to increase friction reducer performance. A multi-functional surfactant blend (MSB) is tested against surfactant formulations commonly used either as a flowback aid or as a performance enhancer for low- cost friction reducers in harsh conditions. A case study is conducted using wells in the Mississippian limestone play to correlate laboratory investigations to field observations.Each surfactant solution was tested with a friction-reducing polymer in synthetic brine containing a salt concentration of 200 000 mg/L representative of harsh field conditions in the laboratory evaluation. Coreflood tests were conducted under reservoir conditions to evaluate flowback efficiency quantified by regained permeability. To test the ability of the surfactants to improve friction reduction (FR) performance, a 0.4 -in. inner diameter friction flow loop was used. In the field- scale application, four wells were hydraulically fractured with two wells acting as control cases and two wells including the addition of the MSB. Completions and production data are presented to compare the performances of the wells and the efficacy of the MSB at the field scale.Friction flow loop testing showed that slickwater fluids with commonly used flowback surfactant formulations, including the MSB, can greatly improve the performance of economical freshwater friction reducers, even in a high calcium (13 000 mg/L) synthetic brine. The same slickwater/surfactant fluids used in the flow loop tests were evaluated in coreflood tests. Depending on the degree of polymer-induced damage created in the core samples, fluids containing the MSB offered the most consistent regained permeability. The laboratory-scale study shows that the MSB is functional for both polymer damage mitigation and acts as a performance booster for the FR, allowing a more economical friction reducer to be selected for slickwater fracturing. In field applications, including the MSB in the fracturing fluid resulted in increased oil production volumes and/or a reduced need for remedial operations throughout the early life of the well.The results of this study show that by properly utilizing the friction flow loop and coreflood laboratory- scale experiments, an optimized MSB can be selected for hydraulic fracturing operations at the field scale. By selecting a flowback surfactant formulation that also increases friction reducer performance, a lower friction reducer dosage or a more economical friction reducer can potentially lead to operational savings at the field scale.
The presence of silos in data and technology of the oil and gas (O & G) production value chain prevents the optimal utilization of resources to enhance production, improve efficiency, and reduce carbon emissions in the O & G production value chain. Real -time optimization of O & G production value chain (ROOPVC) can be used to achieve the above-described objectives. Specifically, ROOPVC allows for i) integration of various elements of the O & G production value chain to create a single reference truth of the system, ii) prediction of unified behavior of the single reference truth using physics-based models and data-driven algorithms, and iii) holistic optimization via single unified digital twin (DT).Based on recent advances, this study reviews system -level and component -level technologies required to implement ROOPVC. Specifically, the study reviews in detail the two major elements of ROOPVC, which are i) DT technology and ii) modeling, simulation, and optimization, respectively. The study also summarizes field experiences in the deployment of ROOPVC. The key challenges, lessons learned, and recommendations for the deployment of ROOPVC are also discussed.The major findings from this review suggest that ROOPVC i) can enable higher stable production while simultaneously allowing significant carbon savings, ii) is suitable for deployment on a field of any size, and iii) can be deployed quickly due to its modular (microservices) approach.
Field and experimental data have shown that perforation erosion during shale gas stimulation invalidates the assumption of a constant coefficient of discharge. However, perforation erosion is not fully understood yet. In this work, a perforation erosion model was built using computational fluid dynamics (CFD) and validated against laboratory data. We then conducted parametric studies to investigate the impact of treatment rate, proppant concentration, proppant size, and fluid viscosity on perforation erosion. Our results demonstrated that a higher treatment rate and larger proppant lead to higher erosion to the perforation diameter. Perforation erosion decreased when fluid viscosities increased from 10 to 100 cp, and then increased when the fluid viscosity was increased to 1,000 cp. Our new understandings could be applied to improve perforation design in shale wells.
Summary Scale precipitation in petroleum equipment is known as an important problem that causes damages in injection and production wells. Scale precipitation causes equipment corrosion and flow restriction and consequently a reduction in oil production. Due to this fact, the prediction of scale precipitation has vital importance among petroleum engineers. In the current work, different intelligent models, including the decision tree, random forest (RF), artificial neural network (ANN), K-nearest neighbors (KNN), convolutional neural network (CNN), support vector machine (SVM), ensemble learning, logistic regression, Naïve Bayes, and adaptive boosting (AdaBoost), are used to estimate scale formation as a function of pH and ionic compositions. Also, a sensitivity analysis is done to determine the most influential parameters on scale formation. The novelty of this work is to compare the performance of 10 different machine learning algorithms at modeling an extremely non-linear relationship between the inputs and the outputs in scale precipitation prediction. After determining the best models, they can be used to determine scale formation by manipulating the concentration of a variable in accordance with the result of the sensitivity analysis. Different classification metrics, including the accuracy, precision, F1-score, and recall, were used to compare the performance of the mentioned models. Results in the testing phase showed that the KNN and ensemble learning were the most accurate tools based on all performance metrics of solving the classification of scale/no-scale problem. As the output had an extremely non-linear behavior in terms of the inputs, an instance-based learning algorithm such as the KNN best suited the classification task in this study. This argumentation was backed by the classification results. Furthermore, the SVM, Naïve Bayes, and logistic regression performance metrics were not satisfactory in the prediction of scale formation. Note that the hyperparameters of the models were found by grid search and random search approaches. Finally, the sensitivity analysis showed that the variations in the concentration of Ca had the highest impact on scale precipitation.
Due to uneven proppant distribution and varied proppant sizes during hydraulic fracturing, artificial fractures of varying length, asymmetry, and varying conductivity are easily formed near the wellbore. The principal focus of this work is to investigate the pressure transient performance of a vertical well penetrated by multiple asymmetrical fractures with varying lengths and varying conductivities in a tight oil reservoir. A novel fracture flow equation was developed specifically to describe the flow behavior inside the complex artificial fractures mentioned above. By combining with the point source solution of the tight oil reservoir, a semianalytical solution was further obtained to analyze the pressure transient behavior of a vertical well with multiple varying-conductivity fractures in a tight oil reservoir. The accuracy and reliability of the newly-developed solution were verified by comparing with the result of a numerical model. With this new solution, fracture flux distribution for different conductivity modes, namely, linearly declining mode, exponentially declining mode, and elliptically declining mode, shows that the near-wellbore fracture flux of the exponential mode is greater than that of the other two modes, but the flux distribution near the fracture tips is on the contrary. Meanwhile, the transient flow characteristics under the above varying conductivity modes indicate that the exponentially varying conductivity has a significant influence on the early linear flow regimes, while the linear and elliptical mode only has a slight influence on the bilinear flow regime under high conductivity. Parameter sensitivity analysis reveals that the obvious inversion point occurring in the pressure derivative curves of uniform conductivity fractures disappears on the pressure derivative curves of varying conductivity fractures, and a weaker asymmetry, a greater adjacent fracture angle, and a larger fracture number and fracture length ratio are conducive to improve the fracturing stimulation effect. This study deepens our understanding of the transient flow performance of vertically fractured wells and helps to estimate artificial fracture properties and evaluate hydraulic fracturing performance.
