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.
Timely detection of leak accidents plays an essential role in the safe operation and risk assessment of natural gas pipelines. However, the scarce leak data and complex operating conditions lead to small samples, data imbalance, and problems with confusing operating conditions. The reliance on leak data limits the recognition performance of the artificial intelligence classification method for leakage operating conditions. A leak detection method based on the unsupervised reconstruction of healthy flow data is established to address these problems. First, an unsupervised neural network is established to reconstruct healthy flow data from real natural gas pipelines. And a model update strategy based on active learning is designed to improve the model's adaptability for time- varying pipelines. Next, a dynamic alarm threshold strategy that accounts for the knowledge of the experience and statistical characteristics of the data segments is suggested to prevent false alarms caused by ambiguous operating conditions. Finally, unlike most recent work that only considers simulated data or laboratory data, this paper conducts a leak case study on an actual natural gas pipeline in service to improve the robustness of the proposed method in the actual operating environment. The findings of this paper can be used as a reference to analyze pipeline behavior analysis based on pipeline flow trend characteristics and early alarm management.
Summary As a mature technology to enhance the permeability of geological formations, hydraulic fracturing has widely been used in geothermal energy development and in the petroleum industry. Due to its effectiveness in practical applications, it attracts many research efforts. Because of the complexity of hydraulic fracturing itself and the complex distribution of stresses around wellbores, accurately describing the behaviors of hydraulic fractures is still a challenging task. In this study, a numerical model is developed to simulate curved propagation of hydraulic fractures from a wellbore, and emphases are placed on influence of in-situ stress and near wellbore stress redistribution. In the developed hydromechanical model, special considerations are given to its ability to simulate curved propagation of hydraulic fractures. The propagation of fractures is modeled through the phase-field method. Several cases on hydraulic fracture initiation and propagation from horizontal wellbores are studied through the proposed model. The model has been successfully verified through analytical solutions. The influence of stress redistribution caused by wellbore pressurization on hydraulic fracture initiation from wellbores is analyzed. Under different in-situ stress configurations and initial fracture orientations (perforation or flaws around wellbores are represented by the initial fractures), several patterns of hydraulic fracture propagation around the wellbores are recognized. It is found that the stress redistribution in the close vicinity of wellbores has great influences on the fracture initiation and propagation, and it makes hydraulic fractures propagate in nonplanar, complex manners. As hydraulic fractures propagate away from the stress redistribution regions around the wellbores, in-situ stress then determines the directions of fracture propagation; the curvature of fracture growth paths is mainly determined by the difference in in-situ stress, for example, σv − σhmin in this study. It has also been demonstrated that, when analyzing fracture propagation from wellbores, the wellbore stability or nonlinear deformation of a wellbore should be considered together with the fracture propagation conditions.
Pseudo-slug flow is a coherent flow pattern that occurs in horizontal or inclined pipes surrounded by segregated and conventional slug flows. Recent studies have demonstrated that this flow pattern can occupy a large area in the flow pattern map and cannot be simply ignored. Churn flow commonly occurs in upward vertical or slightly deviated wellbores characterized by a chaotic intermittent flow behavior. Pseudo-slug and churn flows are generally considered as two different flow patterns mainly because of their visual differences. However, some recent studies have shown that they share many similarities. There are several hydraulic models for churn flow, and the models for pseudo-slug flow have also emerged in recent years. However, these models only work for a certain inclination angle range, and none work for both flow patterns. This paper proposes a new unified hydraulic model that is applicable for both flow patterns. The liquid holdup prediction is based on the drift-flux model, while the pressure gradient is predicted using a two-fluid model that considers wall frictions from both gas and liquid phases. The new model better captures the effects of gas and liquid flow rates, gas density, liquid viscosity, inclination angle, and pipe diameter on the liquid holdup and pressure gradient compared with other models. Model evaluations show that the new model gives the best predictions compared with other available pseudo-slug or churn models. The new model reduces the total average absolute relative error to 14.0% for the liquid holdup prediction and 19.1% for the pressure gradient prediction.
Summary In this study, unique field data analysis and modeling of operating wells with an extended horizontal wellbore (HW) and multistage hydraulic fracturing (MHF) in the Bazhenov formation were conducted. Moreover, a large amount of long horizontal well data obtained from the Bazhenov formation field was used. Wells with extended HW drilling and MHF are necessary for commercial oil production in the Bazhenov formation. Problems can occur in such wells when operating in the flowing mode and using an artificial lift at low flow rates. This study aimed to describe the field experiences of low-rate wells with extended HWs and MHF and the uniqueness of well operations and complexities. It was also focused on modeling various operation modes of such wells using specialized software and accordingly selecting the optimal downhole parameters and analyzing the sensitivity of fluid properties and well parameters to the well flow. The flow rates in wells with extended HW and MHF decrease in the first year by 70–80% when oil is produced from ultralow-permeability formations. Drainage occurs in a nonstationary mode in the entire life of a well, leading to complexities in operation. A comprehensive analysis of field data [downhole and wellhead pressure gauges, electric submersible pump (ESP) operation parameters, and phases’ flow rate measurements] and fluid sample laboratory studies was conducted to identify the difficulties in various operating modes. For an accurate description of the physical processes, various approaches were used for the numerical simulation of multiphase flows in a wellbore, considering the change in the inflow from the reservoir. The complexities that may arise during the operation of wells were demonstrated by analyzing the field data and the numerical simulation results. The formation of a slug flow in low flow rates in a wellbore was caused by a rapid decline in the production rate, a decrease in the water cut, and an increase in the gas/oil ratio (GOR) over time. Based on the results, proppant particles can be carried into the HW and thereby reduce the effective section of the well in case of high drawdowns in the initial period of well operation. Consequently, the pressure drops along the wellbore increased, and the drawdown on the formation decreased. Other difficulties were determined to be associated with the consequences and technologies of hydraulic fracturing (HF). These effects were shown based on the field data and the numerical simulation results of the flow processes in wells. In addition, corrective measures were established to address various complexities, and the applications of these recommendations in the field were conducted.
Electrical submersible pumps (ESPs) are an important artificial lift method used in oil production. ESPs can provide high production flow rate, are flexible, and can be installed in highly deviated wells, subsea deepwater wells, or on the seabed. ESP performance is generally characterized by manufacturers using only water as fluid. However, oil properties are very different from water and significantly alter the pump's performance. Operating ESPs with viscous fluids leads to degraded pump performance. Therefore, knowing the ESP's performance when pumping viscous fluid is essential to properly design the production system. In this work, we present an experimental study of ESP performance operating with viscous flow. A total of six ESP models were tested, operating at four different rotational speeds and 11 viscosities, resulting in a comprehensive database of more than 5,800 operating conditions. This database contributes to the literature given the lack of available data. We also perform a phenomenological analysis on the influence of operational parameters, such as viscosity, rotational speed, specific speed, and rotational Reynolds number. The database and analyses performed are central for future models predicting the viscous performance of ESPs. The results from our investigation and tests showed that the increase in viscosity causes (1) a reduction in the head and (2) an increase in drive power, resulting in (3) a sharp decrease in efficiency. However, increasing rotational speed tends to mitigate this performance degradation. Efficiency and flow rate correction factors are virtually independent of the flow rate within the recommended operating region. This is not true for the head correction factor, which is not constant. The pump geometry seems to influence its performance as ESPs with higher specific speed are less impaired by viscous effects. The database obtained in the present work is available in the data repository of the University of Campinas, at the address presented by Monte Verde et al. (2022).
Summary With atmospheric methane concentrations rising and spurring increased social concern, there is a renewed focus in the oil and gas industry on methane emission monitoring and control. In 2019, a methane emission survey at a bp asset west of Shetland was conducted using a closed-cavity methane spectrometer mounted onboard a long-endurance fixed-wing uncrewed aerial vehicle (UAV). This flight represents the first methane emissions survey of an offshore facility with a miniature methane spectrometer onboard a UAV with subsequent flights performed. A small laser spectrometer was modified from an open-cavity system to a closed-cavity onboard the aircraft and yielded in-flight detection limits (3 seconds) of 1,065 ppb methane above background for the 2019/2020 sensor version and 150 ppb for the 2021 sensor versions. Through simulation, the minimum detection limits of sensors in mass flow rate were determined to be 50 kg/h for the 2019/2020 campaign and 2.5 kg/h for the 2021 campaigns, translating to an obtainable measurement for 23% and 82% of assets reporting higher than 1 kg/h according to the 2019 Environmental Emissions Monitoring System (EEMS) data set, respectively. To operationalize the approach, a simulation tool for flight planning was developed using a Gaussian plume model and a scaled coefficient of variation to invoke expected methane concentration fluctuations at short time intervals. Two methods were developed to calculate offshore facility-level emission rates from the geolocated methane concentration data acquired during the emission surveys. Furthermore, a Gaussian plume simulator was developed to predict plume behavior and aid in error analysis. These methods are under evaluation, but all allow for the rapid processing (<24 hours) of results upon landing the aircraft. Additional flights were conducted in 2020 and 2021 with bp and several UK North Sea operators through a Net Zero Technology Centre (NZTC)-funded project, resulting in a total of 18 methane emission survey flights to 11 offshore assets between 2019 and 2021. The 2019 flight, and subsequent 2020–2021 flights, demonstrated the potential of the technology to derive facility-level emission rates to verify industry emission performance and data.
Green demulsifier was synthesized through an esterification technique by using polyethylene glycol (PEG) and fatty acid (lauric acid). The synthesized demulsifier was characterized through several tests to analyze the functional groups and determined the molecular structure, thermal stability, and biodegradability of the demulsifier molecule. The performance of the synthesized demulsifier was investigated using the standard static bottle test method to break the water -in -oil (W/O) tight emulsion. Optical microscopic and viscosity studies of emulsions were also performed to understand the demulsification process and mechanism. Based on the response surface method (RSM), central composite design (CCD) was used to develop the statistical model of demulsification efficiency by considering the four most influencing factors-demulsifier concentration, water content, settling time, and temperature-and to examine the optimal condition for maximum water separation from the emulsion. The statistical model's accuracy and significance were evaluated using analysis of variance (ANOVA) and diagnostic plots. The effect of each factor was analyzed through 3D graphs and contour maps. The result indicates that all the factors significantly influenced the demulsification efficiency with ap- value of <0.0001, among which the presence of water is the dominating variable. At the optimal condition, the lauric acid- PEG-demulsifier (LPED) achieves a maximum demulsification efficiency of 95% in 30.9 minutes. Furthermore, the percent absolute deviation was computed after comparing the experimental findings to those predicted by the model and it was observed that the model's prediction accuracy was >97%. Finally, the biodegradability test results showed that the developed demulsifier is completely biodegradable in 21 days. Because the synthesized demulsifier is eco-friendly and has an excellent dehydration rate, it may be used in the petroleum industry for breaking field -tight emulsions as an alternative to chemical demulsifiers.
Summary Batch transportation of oil and water is a new transportation method in oil and gas gathering and transportation pipelines. Its corrosion inhibition effect has been preliminarily verified in a horizontal pipe experiment. However, achieving overall visualization in traditional loops is difficult, resulting in limited flow pattern classification and analysis of influencing factors. Combining the advantages of the traditional flow loop and the wheel flow loop, we introduce in this paper a round-head straight pipe loop and analyze the influence of key factors on the evolution of the flow pattern of the oil-water interface and the dimensionless length of the oil-water film (L~o, L~w) on the pipe wall through computational fluid dynamics (CFD) numerical simulation. The results show that the batch transportation of oil and water using the round-head straight pipe loop is more in line with the flow characteristics of oil and water two-phase flow in gathering pipelines. Three distinct three-layered flow patterns were identified, which are Flow Pattern I (oil-in-water in the upper layer, annular flow in the middle layer, and oil as the annular phase, water as the core phase, and oil-in-water in the lower layer, abbreviated as DW/O-AN-DW/O), Flow Pattern II (oil phase in the upper layer, annular flow in the middle layer, water as the annular phase, oil as the core phase, and oil in the lower layer, abbreviated as O-AN-O), and Flow Pattern III (oil phase in the upper layer, water-in-oil dispersion flow in the middle layer, and oil in the lower layer, abbreviated as O-DO/W-O). Additionally, parametric analysis reveals that the velocity of the rigid body (ν) has the greatest influence on the coverage rate of the oil film on the pipe wall, followed by the viscosity of crude oil. The density of crude oil has the least influence. The round-head straight pipe loop model offers an accurate simulation of the process of oil and water batch transportation in actual production pipelines. Therefore, the corrosion mitigation efficiency increases with the increase in oil viscosity when the viscosity of the oil lies within the range of 0.01–1 Pa·s. This increase is due to the formation of a more stable oil film on the pipe wall at higher viscosities. When the speed of the rigid body ranges from 0.5 to 1 m/s, due to the small fluid velocity, the erosion effect on the oil film on the pipe wall is relatively small, and the corrosion mitigation efficiency remains stable within a wide range.
Hydraulic fracturing in limited -entry (LE) completion designs relies on maintaining a high bottomhole treating pressure (BHTP). LE requires high perforation friction to maintain an even distribution of the hydraulic fracturing slurry. As sand exits the perforations, the perforations start to erode. The erosional change in the perforation alters the desired perforation friction and subsequent BHTP. As operators rely on multistage hydraulic fracturing to generate economic production, the issue of perforation erosion becomes inherently repetitive from stage to stage and cumulatively a significant issue. The industry has seen how a perforation can change from a before -and -after perspective with downhole cameras and imaging techniques before and after treatments. However, a more detailed understanding of the dynamic process of perforation erosion can give a better expectation of perforation performance throughout a hydraulic fracturing treatment and not just pretreatment compared to post-treatment.Computational fluid dynamics (CFD) is a quickly emerging tool in the industry. CFD aims to model fluid flow by numerically solving the Naiver-Stokes equations within a specified domain. Along with modeling fluid systems, CFD has the capability to model dispersed particles within the fluid. Once the particles are introduced into the fluid, the domain can also be eroded away within the CFD model. By utilizing the erosional capabilities of CFD, paired with the flow of a hydraulic fracturing slurry, perforation erosion can be investigated transiently throughout an entire hydraulic fracturing stage.This work presents a better dynamic understanding of perforation erosion rather than just a "before vs. after" comparison. The CFD modeling methodology used to achieve the correct erosional pattern observed in the field is presented. Throughout this work, four dif-ferent hydraulic fracturing completion parameters are investigated to determine the respective roles in perforation erosion. The four parameters include proppant size, proppant concentration, fracturing fluid viscosity, and proppant concentration ramping schedules. By investigating the impact that controlled design parameters have on perforation erosion, perforation erosion can be better anticipated to deliver improved completion results.
Summary The purpose of this paper is to provide additional information and insights gained on manuscript SPE-209980-MS, accepted for presentation at the 2022 Society of Petroleum Engineers Annual Technical Conference and Exhibition (Esparza et al. 2022). The energy sector has been identified as one of the main contributors to emissions of anthropogenic greenhouse gases. Therefore, sustainability in the sector is mainly associated with the advancement in environmental and social performance across multiple industries. Individual firms, particularly those belonging to the oil and gas (O&G) industry, are now assessed for their environmental, social, and governance (ESG) performance and their impact on climate change. To meet the different key performance indicators (KPIs) for corporate social responsibility (CSR) and ESG, the planning, development, and operation of O&G infrastructure must be conducted in an environmentally responsible way. Today, operators calculate their own emissions, which are typically self-reported annually, usually relying on emission factors to complement the lack of emission measurement data. This paper discusses how methane detection of O&G infrastructure using remote sensing technologies enables operators to detect, quantify, and minimize methane emissions while gaining insights and understanding of their operations via data analytics products. The remote sensing technologies accounted for in this paper are satellite and aerial platforms operating in tandem with data analytics, providing a scheme to support sustainability initiatives through the quantification of some ESG metrics associated with methane emissions. This paper presents examples of measurements at O&G sites taken with satellites and aircraft platforms, providing evidence of methane emissions at the facility level. A discussion of each platform and how they work together is also presented. Additionally, this paper discusses how these data insights can be used to achieve sustainability goals, functioning as a tool for ESG initiatives through the incorporation of analytical models.
Distributed acoustic sensing (DAS) is an emerging surveillance technology that is becoming increasingly popular in the oil and gas indus -try for real -time flow monitoring. However, there are limited studies that rigorously quantify flow rates using DAS. This work expands the existing literature by presenting a detailed workflow for accurately estimating fluid flow rates from DAS data using time-and frequency-domain signal processing. Three simple empirical correlation functions (linear, exponential, and cubic) are developed and tested to pre-dict flow rates from DAS. The proposed correlations are demonstrated for flow rates ranging from 50 to 300 gallons per minute (GPM) in a vertical 5,163-ft- deep wellbore and from 12 to 36 GPM in a horizontal surface flow loop. Tests were performed using a single- phase flow of water as well as using synthetic oil- based drilling mud. Time-domain DAS processing using root- mean-square (RMS) value and frequency-domain processing using frequency band energy (FBE) is evaluated, followed by a statistical approach to minimize the influ-ence of outliers. The RMS and FBE approaches are individually compared for flow prediction, and the performance of the correlations is rigorously evaluated on a blind data set that was not originally used for developing the correlations. For both the wellbore and flow loop data sets, a coefficient of determination (or R2) greater than 0.95 with an average flow rate prediction error of less than 10% was achieved for the best-performing correlation for the blind test data. The analysis procedure and workflow presented in this study can be adopted and extended to different operating conditions for quantitative flow rate prediction using DAS.
Summary The evaluation of geomechanical effects and fluid flow related to pressure transient phenomena in fractured Biot’s stress-sensitive oil reservoirs is essential to minimize the mechanical formation damage and extend the well-reservoir life cycle for economical production. Therefore, the management of the damage caused by effective permeability loss in this type of reservoir becomes essential to productivity maintenance. This paper proposes a new unsteady-state poroelastic solution for the nonlinear hydraulic diffusivity equation (NHDE) in Biot’s effective stress-sensitive reservoirs fully penetrated by fractured oil wells. The hydraulic fracture in the proposed mathematical modeling is finite with tip effects and crosses the whole reservoir net-pay. A new permeability stress-sensitive pseudopressure m(σ′) is developed, and the solution of the NHDE is derived in terms of this function. The NHDE is expanded in a first-order asymptotic series, and a poroelastic integro-differential solution coupled to a Green’s function is used to represent the source/sink term. A set of pore pressure and permeability data is used from geomechanical literature and transformed into effective stress through Biot’s equation. The effects of the Biot’s coefficient, overburden stress, oil flow rate, fracture’s tip, and proppant porosity arrangements are simulated. The results show that these parameters are essential to minimize formation damage. Model calibration is performed using a numerical oil flow simulator named IMEX®, widely used in the oil industry. The accuracy, ease of implementation, and low computational costs constitute the main advantages of the model addressed in this paper. Hence, it may be a valuable and attractive mathematical tool to identify flow regimes, providing permeability loss control and supporting well-reservoir management.
Summary To improve the overall performance of continuous spiral baffle heating systems, we propose in this paper two different structural models of electric heaters for in-situ shale oil wells. The models are simulated using Fluent software to investigate the flow and heat-transfer characteristics under different mass flow rates. The variation in heat-transfer coefficient, pressure drop, and overall performance of the heating plate under different gas mass flow rates and heights of heating and shielding plates are analyzed. The performance of the two different heater structures is compared with Wang’s laboratory experiment. The results show that Model I of the heating system has the best overall performance when the gas mass flow rate is between 9.74×10−3 kg/s and 1.624×10−2 kg/s, and the height of the heating and shielding plates is 35 mm at a mass flow rate of 9.74×10–3 kg/s. Wang’s pressure drop (ΔP) is more than 2.48 times higher than that of Model I and more than 6.49 times higher than that of Model II, while the heat-transfer coefficient (h) of both Model I and Model II is increased by more than 15% compared to Wang’s experiment. The overall performance (g) of Model II is increased by more than 5.7 times compared to Wang’s experiment, and the overall performance (g) of Model II is increased by more than 1.68 times compared to Model I. These results provide a theoretical basis for optimizing the design of continuous spiral baffle heating systems.
Summary To solve the problem of poor salt resistance of conventional drag reducers, a hydrophobic associative polymer drag reducer was prepared by inverse emulsion polymerization with acrylamide (AM), methacrylic acid (MAA), 2-acrylamide-2-methylpropane sulfonic acid (AMPS), and hexadecyl dimethyl allyl ammonium chloride (C16DMAAC) as the main monomers. The synthetic product was confirmed as the target product by infrared (IR), nuclear magnetic resonance (NMR), and mass spectrometry (MS). The viscosity-average molecular weight of the prepared drag reducer is 1100×104 g/mol. The pipeline friction results show that the drag reducer has good friction reduction and salt resistance. When the concentration in clean water is 0.06%, the maximum friction reduction rate is 71.1%. When the salinity is 5×104 mg/L, the calcium ion concentration is 2000 mg/L, and the suspended solid content is 500 mg/L, the maximum friction reduction rate is 68.9% when the concentration of the drag reducer is 0.06%. Salt water will not significantly lower the friction reduction rate. If the concentration of the drag reducer is increased to 0.08%, the maximum drag reduction rate will reach 73.8%. The microrheological test results of the friction reducer solution show that, at 0.2% concentration, there is no network structure between friction reducer molecules, which is consistent with Newtonian fluids possessing a certain viscosity. The elasticity index (EI) of the drag reducer solution is basically unchanged over time, maintaining good friction reduction and sand-carrying performance during the shearing process of large displacement pumping.
Industrial systems are becoming more sophisticated, and their failure can result in significant losses for the company in terms of production loss, maintenance costs, fines, image loss, etc. Conventional approaches to modeling and evaluating the failure mechanisms of these systems do not consider certain important aspects, such as the interdependencies between failure modes (FMs) with information and data containing uncertainties as they are generally collected from experts' judgments. These restrictions may lead to improper decision making. The use of more advanced techniques to model and assess the interdependencies among components' failures under uncertainties seems to be more than necessary to overcome these deficiencies.It is in this context that the proposed approach fits. It consists of proposing a hybrid multicriteria decision- aking (MCDM) approach that combines several techniques for a better selection of maintenance strategies. Using the failure mode and effects analysis (FMEA) technique, the potential FMs of components, along with their causes and effects, are identified. The relative importance (or weight) of these FMs is determined using the fuzzy simple additive weighing (FSAW) method based on how they affect the system's goals. The causal relationships between FMs and their final weights are determined by the fuzzy cognitive maps (FCM) method and the nonlinear Hebbian learning and differential evolution (NHL DE) algorithm. Finally, based on the final FM weights provided by the FCM, the simple additive weighing (SAW) method is used to select the optimal maintenance strategies. The results of applying the proposed approach to an operating compressor lubrication and sealing oil system demonstrate its importance and usefulness in assisting system operators to efficiently allocate the optimal maintenance strategies, considering the strong correlation between FMs and their effects on system performance while accounting for the uncertainties associated with experts' judgments. These correlation effects have led to changes in the assigned weights of the selected FMs. Specifically, the FM related to the low output of the lube/seal oil pump, which was initially assigned a lower priority, and with the correlation effects has become the first critical FM. This shift in prioritization emphasizes the need to address this particular FM promptly. By focusing on addressing these high-priority FMs, maintenance efforts can be optimized to prevent or mitigate more severe consequences. Among the various maintenance strategies evaluated, it was determined that the combination of condition based maintenance (CBM) and precision maintenance (PrM) yields the most favorable outcome in terms of mitigating the impact of accidental failures and undesired events on the selected system.
