This review addresses the diverse applications of multiphase flows, focusing on drilling, completions, and injection activities in the oil and gas industry. Identifying contemporary challenges and suggesting future research directions, it comprehensively reviews evolving applications in these multidisciplinary topics. In drilling, challenges such as gas kicks, cutting transport, and hole cleaning are explored. The application of immersion cooling technology in surface facilities for gas fields utilized in integrated bitcoin mining is also discussed. Nanotechnology, particularly the use of nanoparticles and nanofluids, shows promise in mitigating particulate flow issues and controlling macroscopic fluid behavior. Nanofluids find applications in drilling for formation strengthening and mitigating formation damage in completions as highlighted in this work, as well as in subsurface injection for enhanced oil recovery (EOR), waterflooding, reservoir mapping, and sequestration tracking. The review emphasizes the need for techno-economic analyses using multiphase flow models, particularly in scenarios involving fluid injection for energy storage. Addressing these multiphase flow challenges is crucial for the future of energy diversity and transition initiatives, offering benefits such as financial stability, resilience, sustainability, and reliable supply chains. The first part of this review presents the application of multiphase (typical gas, liquid, solid) flow models and technology for drilling, completion, and injection operations. While the second part reviews the applications of multiphase particulate (nanofluid) flow technology, the use of computational fluid dynamics (CFD), machine learning (ML), and system modeling for multiphase flow models in drilling, completions, and injection operations.
This review addresses the diverse applications of multiphase flows, focusing on drilling, completions, and injection activities in the oil and gas industry. Identifying contemporary challenges and suggesting future research directions, it comprehensively reviews evolving applications in these multidisciplinary topics. In drilling, challenges such as gas kicks, cutting transport, and hole cleaning are explored. The application of immersion cooling technology in surface facilities for gas fields utilized in integrated bitcoin mining is also discussed. Nanotechnology, particularly the use of nanoparticles and nanofluids, shows promise in mitigating particulate flow issues and controlling macroscopic fluid behavior. Nanofluids find applications in drilling for formation strengthening and mitigating formation damage in completions as highlighted in this work, as well as in subsurface injection for enhanced oil recovery (EOR), waterflooding, reservoir mapping, and sequestration tracking. The review emphasizes the need for techno-economic analyses using multiphase flow models, particularly in scenarios involving fluid injection for energy storage. Addressing these multiphase flow challenges is crucial for the future of energy diversity and transition initiatives, offering benefits such as financial stability, resilience, sustainability, and reliable supply chains. In the first part of this review, we presented the application of multiphase (typical gas, liquid, solid) flow models and technology for drilling, completion, and injection operations. This second part of this review presents the applications of multiphase particulate (nanofluid) flow technology for drilling, completion, and injection operations. It aims to identify technology development needs related to multiphase flows, enhancing research endeavors for better cognition and mitigation of the identified issues. The use of computational fluid dynamics (CFD), machine learning (ML), and system modeling for multiphase flow models is also discussed.
Wax deposition in oil and gas pipelines and equipment is a fundamental challenge that can lead to a decrease in the performance and useful life of these systems. To address this issue, various methods have been developed to reduce wax deposition. This article investigates two novel methods, namely microwave and ultrasonic, for wax deposition mitigation. The microwave method utilizes high-frequency electromagnetic waves and short wavelengths to transfer heat to the wax and separate it from the internal surface of the pipelines. In this method, microwave waves provide energy to the wax, increasing its temperature and causing it to melt and move. Due to its speed, efficiency, and applicability in industrial environments, the microwave method has been recognized as a leading approach in wax deposition reduction, requiring minimal modifications to the pipeline structure. The ultrasonic method employs high-frequency sound waves to disrupt and prevent wax deposition. Ultrasonic waves generate alternating pressure waves at the site of wax accumulation, breaking down the wax structure. This non-destructive and reliable method is capable of reducing wax deposition in hard-to-reach areas. Both microwave and ultrasonic methods have gained attention as innovative approaches for wax deposition reduction. However, further research is needed to optimize and enhance these methods, aiming to improve their implementation capabilities, increase efficiency, and reduce costs. The study also addressed conventional and common methods, such as insulators heat-proofing materials, heating techniques to prevent wax deposition, cold flow, wax inhibiting tools, wax removal techniques, chemicals, and bacterial treatment.
The upper Dalan and Kangan or Permian-Triassic carbonate formations in the central the Persian Gulf are considered as world's giant gas reservoirs. The primary purpose of this research is to model and evaluate the relationship between hydraulic flow units (HFUs), electrofacies and microfacies with systems tracts of Permian-Triassic sequences. By integrating the results of core data, petrographic studies, and petrophysical logs of the studied formations, hydraulic flow units and electrofacies were identified. Based on the results of petrographic studies, twelve microfacies were identified in terms of textural and depositional characteristics. Based on depositional setting, sedimentary facies and INPEFA values obtained from gamma ray log and gamma deviation log (GDL) in the context of sequence stratigraphy, zonation of Dalan and Kangan reservoirs is carried out. The zonation boundaries correspond to the key stratal surfaces (sequence boundary and maximum flooding surface). Seven petrographic rock types (PRT) were identified for the upper Dalan-Kangan reservoirs based on sedimentary texture, diagenetic process and dominant pores. Using porosity and permeability data from the core analysis, five hydraulic flow units were identified based on the flow zone indicator (FZI) method. Using multi-resolution graph-based clustering (MRGC) four electrofacies were detected from petrophysical data (gamma, neutron, density and acoustic logs). Subsequently, the INPEFA, GDL and electrofacies were spatially modeled using the sequential indicator simulation (SIS) and sequential Gaussian simulation (SGS) geostatistical methods. Finally, a clear agreement was revealed between the reservoir zones and the stratigraphic sequence framework. It this regard, the microfacies belonging to the high-energy and grain-dominated settings (packstone, grainstone) of leeward shoal, shoal and seaward shoal belts have the best reservoir units due to the influence of dissolution and dolomitization. The best reservoir units in the Permian-Triassic deposits in the middle of the upper Dalan and lower Kangan are developed in UDS4, upper KS2 and middle KS1 units. On the other hand, mud-dominated facies (mudstone, wackestone) and anhydrite textures are mostly associated with the low-energy lagoonal environments, between tidal flat and Sabkha. Non-reservoir units have been formed in the upper Dalan/Kangan and in the transgressive systems tract of UDS3-a, KS2-a and the lower and upper part of KS1 transgressive-highstand systems tract.
Drilling motors are widely used in unconventional oil and gas exploration. Due to the increased non-productive time and drilling costs brought about by accidental damage to drilling motors, predictive maintenance for drilling motors is necessary to optimize asset utilization. However, service companies face significant challenges in achieving predictive maintenance: operational data acquisition, automated statistics analysis, and drilling state recognition. This paper presents a miniature vibration recorder, an automatic statistical analysis method, and a layered recognition algorithm to resolve these challenges and improve tool maintenance efficiency. The designed recorder can be installed in the catch of a conventional mud motor to record drilling dynamics over a drilling motor's entire operation cycle. Time-series data from the recorder can be used to automatically generate operation statistics, mitigating the costs incurred by manual data analysis. The layered recognition algorithm then enables the automatic identification of drilling operation states, i.e., surface, downhole non-drilling, downhole sliding, and downhole rotation. The solutions were validated by deploying the recorder in drilling field runs and analyzing recorded data using the associated design software, yielding a functional data collection, automatic data statistical analysis, and operation state recognition accuracy of 95%. Through achieving improved data collection and analysis, the recorder and software introduced in this work can notify motor owners of the detailed operation history of their tools and enable informed preventive maintenance.
Accurate characterization of crude oils by determining the composition of saturates, aromatics, resins and asphaltenes (SARA) has always been a challenging task in the petroleum industry. However, conventional experimental methods for determination of SARA composition are labour intensive, time-consuming and expensive. In the present study, artificial neural network (ANN) models were developed to predict the SARA composition from easily measurable parameters like density and viscosity. A dataset of 216 crude oil samples covering wide range of geographical locations was compiled from various literature sources. The ANN models with one hidden layer and six neurons are trained, tested and validated using MATLAB neural network toolbox. Results obtained on analysis revealed reasonably good accuracy of prediction of SARA components except for aromatics. The performance of developed ANN models was compared with various correlations reported in literature and found to be better in terms of mean squared error and coefficient of determination. The developed models hence provide a cost-effective and time-efficient alternative to the conventional SARA characterization techniques.
Carbonate gas reservoirs generally contain water, leading to uneven water invasion, explosive water flooding and other prominent phenomena, which is an important factor restricting the efficient development of gas reservoirs. The study of gas-water two-phase flow behavior in carbonate gas reservoirs is of great significance for understanding the formation mechanism of residual water and trapped gas and improving the recovery of gas reservoirs. In this study, microscopic visualization physical models of fractured-vuggy and fractured-porous types were established based on CT images. And then gas-water two-phase flow experiments were conducted using the models, visually presenting the characteristics of gas-water two-phase flow and the formation mechanism of residual water and trapped gas in such reservoirs. On the basis of experiments, numerical simulation of gas-water two-phase flow at pore scale under high-temperature and high-pressure conditions was conducted using the VOF method, and the effect of capillary number on gas-water two-phase flow was quantitatively evaluated. The experiment results indicate the types of residual water and trapped gas formed in the fractured-vuggy and fractured-porous reservoirs. Compared with fractured-vuggy reservoir, the type of residual water in fractured-porous reservoir doesn't include water masses in the vugs, but includes network shaped residual water, and the type of trapped gas also includes network shaped trapped gas. The numerical simulation results indicate the residual water in the fractured-porous reservoir decreases with the increase of capillary number during gas flooding process, while the distribution of residual water in the fractured-vuggy reservoir is influenced by the combination of fractures and vugs. The distribution of trapped gas in different types of reservoirs shows a trend of first decreasing and then increasing with the increase of capillary number during water flooding process. The results in this study can provide theoretical support for revealing the formation mechanism of residual water and trapped gas in carbonate gas reservoirs and improve gas recovery.
During the water injection process in oil fields, the original stress equilibrium of faults might be disrupted by the large influx of fluids into the reservoir, resulting in movements. Fault slip may lead to the leakage of underground oil and gas, in addition to impairing the integrity of wellbore and casing. To explicate the mechanism of fault slip caused by fluid injection, as well as quantify the fault slippage, A geomechanical finite element model on a reservoir scale is established. This model combines the information regarding the geological structural geometric characteristics and the mechanical properties of rocks at varying depths. Based on the TSL (traction-separation law), we utilize the cohesive contact method to depict the cohesive mechanical strength of the fault gouge and its damage evolution process. Consequently, we simulate and examine the reactivation and slip laws of the fault, which induced by the reduction of cohesive strength and frictional strength post water injection. The research results illustrate that the relatively stable state of the fault primarily relies on the cementation of fault gouge. As the volume of fluid invading the fault area increases, the shear cementation strength of fault gouge diminishes, resulting in the complete activation of the fault. Afterwards, the fault starts slipping. The continued slip post-reactivation of the fault is influenced by the frictional strength of the fault plane. As the friction coefficient of the fault plane drops, the average slip distance of the fault rises. This investigation offers important insights into the impact of fluid injection on fault behavior and can guide the design of injection operations in oil fields.
Hydraulic fracturing creates multiple induced fractures and micro-fractures, forming a complex fracture network in the reservoir. The study of the transport and distribution of the proppant within the fracture network is critical to the design and evaluation. However, existing simulation studies of proppant transport tend to be overly idealized and neglect the inhomogeneity of fracture widths that occur after fracturing. To address these issues, this study employs computational fluid dynamics (CFD) to study the transportation of fracturing fluid and proppant within a fracture network. The flow dynamics of solid-liquid two-phase flow in fractures are simulated using the Euler-Euler multiphase flow model. Considering the actual variables in field construction and the inherent inhomogeneity in realistic fracture structures, a three-dimensional model was established to capture the gradual variation in fracture width. The accuracy of this model was verified through a comparative analysis with physical experiments. On this basis, an investigation was conducted to explore the impact of particle size, particle density, particle volume concentration, and injection velocity on proppant transportation. The results demonstrate that, in contrast to conventional rectangular fractures, sandbanks formed from wedge fractures exhibit a lower height, which facilitates improved transportation into deeper fractures. Furthermore, particle concentration primarily influences distal fractures, with proppant particle size being second. The injection velocity has a significant impact on the height of the sandbank located in proximity to the fracture inlet. The research findings provide a deeper understanding of the transport and distribution of proppants within wedge fractures, thereby establishing a theoretical basis for the analysis and engineering guidance in on-site hydraulic fracturing construction.
A novel numerical model is established to study the hydraulic fracture extend in poroelastic media with natural fractures based on the phase field method. In this new model, the poroelasticity parameter (Biot's coefficient, Biot's modulus, and porosity) of rock is a function of the phase field value. Therefore, a new phase field evolution equation is derived. The finite element numerical discretization method and Newton-Raphson (NR) iterative method are adopted to establish the corresponding numerical solution iterative scheme. The stability and correctness of the model were verified by a series of numerical simulation cases. The fluid pressure within the fracture, the fracture length, and the fracture width calculated by the model that regards the poroelasticity parameter as a constant would be larger, longer, and smaller, respectively, compared with those calculated by the model established in this study. The effect of certain formation factors and engineering factors on the intersection behavior between hydraulic fracture and natural fracture is investigated based on the established model.
Surfactant flooding is a well-known chemical approach for enhancing oil recovery. Surfactant flooding has the disadvantage that it cannot withstand the harsh reservoir conditions. Improvements in oil recovery and release are made possible by the use of nanoparticles and surfactants and CO2 co-injection because they generate stable foam, reduce the interfacial tension (IFT) between water and oil, cause emulsions to spontaneously form, change the wettability of porous media, and change the characteristics of flow. In the current work, the simultaneous injection of SiO2, Al2O3 nanoparticles, anionic surfactant SDS, and CO2 in various scenarios were evaluated to determine the microscopic and macroscopic efficacy of heavy oil recovery. IFT (interfacial tension) was reduced by 44% when the nanoparticles and SDS (2000 ppm) were added, compared to a reduction of roughly 57% with SDS only. SDS-stabilized CO2 foam flooding, however, is unstable due to the adsorption of SDS in the rock surfaces as well as in heavy oil. To assess foam's potential to shift CO2 from the high permeability zone (the thief zone) into the low permeability zone, directly visualizing micromodel flooding was successfully executed (upswept oil-rich zone). Based on typical reservoir permeability fluctuations, the permeability contrast (defined as the ratio of high permeability to low permeability) for the micromodel flooding was selected. However, the results of the experiment demonstrated that by utilizing SDS and nanoparticles, minimal IFT was reached. The addition of nanoparticles to surfactant solutions, however, greatly boosted oil recovery, according to the findings of flooding studies. The ultimate oil recovery was generally improved more by the anionic surfactant (SDS) solution including nanoparticles than by the anionic surfactant (SDS) alone.
The chitosan oligosaccharide and vanillin are used as raw materials to synthesize non-toxic and water-soluble chitosan oligosaccharide derivative called vanillin chitosan oligosaccharide as a green corrosion inhibitor. The corrosion inhibition properties of 20# steel in CO2-saturated solution system at 25°C and 3.5 wt% NaCl were studied. The synergistic effect of potassium iodide (KI) and VCOS on corrosion inhibition was also studied. Various techniques such as weight loss (WL), electrochemical analysis, atomic force microscopy (AFM), scanning electron microscopy (SEM), quantum chemical calculations, and molecular dynamics simulations were used to understand the inhibition properties. The inhibition efficiency of VCOS enhanced remarkably after the addition of KI, reaching an optimum value of 93.1%. EIS results showed that the inhibition of VCOS + KI on metal surface increased with time. Polarization measurements showed that VCOS and KI acted as mixed inhibitors by demonstrating anode dominance. SEM and AFM were used to study the formation of inhibition film on metal surface after 3 days immersion. We concluded that the mixed inhibitor followed Langmuir adsorption isotherm. In addition, quantum chemistry and molecular dynamics simulations were used to verify the relationship between corrosion inhibition efficiency and molecular structure.
This study evaluated the effect of monovalent and divalent ions and the dosage of a SiO2-based nanocomposite on the thermochemical stability of HPAM polymeric solution. Chelating amine-functionalized NPs (AFNPs) were used to enhance the thermochemical stability of HPAM based on capturing monovalent/divalent ions after seven days at 70°C. Different polymer solutions prepared with calcium chloride dihydrate (CaCl2·2H2O) at 2000 mg/L and sodium chloride (NaCl) at 10000 mg/L, and two different dosages of HPAM (1000 and 2000 mg/L) were assessed in the presence and absence of AFNPs at dosages of 200, 500 and 1000 mg/L. The nanocomposite was characterized by N2 adsorption, Fourier-transformed infrared spectrophotometry (FTIR), thermogravimetric analysis (TGA), dynamic Light Scattering (DLS), and Zeta potential (ZP). Stability tests over time confirmed the positive effect of nanocomposite on increasing the thermochemical stability of polymer solutions. Results revealed that adding 0, 200, and 500 mg/L of nanocomposite to the polymeric solution at 1000 mg/L of HPAM, 10000 mg/L of NaCl, and 2000 mg/L of CaCl2·2H2O led to the viscosity reductions of 73.5%, 18%, and less than 1% after 7 days (70°C), respectively. Nanocomposite at 200 mg/L reduces the polymer degradation in the presence of the two salts evaluated separately, i.e., 20% for 10000 mg/L of NaCl and 15% for 2000 mg/L of CaCl2·2H2O. The adsorption tests on AFNPs and SiO2 NPs concluded that AFNPs had higher adsorption of cations in comparison to SiO2 NPs and that greater adsorption of cations is related to a reduction in polymer degradation.
In recent years, the risk assessment of well control equipment has faced some problems, such as shallow defect detection depth, large identification error of corrosion defect type, inaccurate equipment corrosion assessment, and so on. To solve the above problems, a corrosion defect classification and identification model based on an improved K nearest neighbor algorithm (KNN) is established for the well control pipeline in well control equipment. Firstly, the pulsed magnetic flux leakage (PMFL) sensor is used to detect the pipeline defects, and then the collected data are denoised. Then, the corrosion type identification model of well control pipeline based on K-means++ and KNN is established. Finally, the corrosion risk of well control pipeline is evaluated according to the type of corrosion output from the identification model. The experimental results show that the improved algorithm has high accuracy in identifying the corrosion type of well control pipeline, and the calculation speed is better than other algorithms described in this paper.
In petroleum engineering, the performance of drilling fluid is the key factor affecting the drilling success. Drilling fluid rheology can be measured by tube measurement. Fluid pulsation will cause measurement deviation of differential pressure and flow velocity data during measurement, and it accumulates when the flow curve is drawn. Finally, the accuracy of drilling fluid rheological pipe measurement is seriously affected. In view of the problem of fluid pulsation can seriously affect the accuracy of tube measurement. This paper proposed an algorithm based on Filtered-x least mean square (FxLMS). First, the active control strategy is studied, the mathematical model of electric regulating valve control is established, the FxLMS algorithm of variable step length is studied, the simulation model of the control system is established, and the control effect of different algorithms is compared. The dynamic experimental platform of fluid pulse active control for drilling fluid rheological pipe measurement is designed and built. The experimental data show that: after active control, the average relative error of drilling fluid shear force decreased by 179.6%, the average relative error of plastic viscosity decreased by 78.1%, and the average relative error of the apparent viscosity decreased by 25.5%. It proves that the active control algorithm can improve the accuracy of tube measurement more effectively.