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Collaborating Authors
The University of Oklahoma
Summary Barite sag causes pressure fluctuations in the wellbore, which is undesirable. These problems usually occur with oil-based muds (OBMs; invert emulsion muds) and are associated with fluid properties and operation parameters. Drilling issues related to this undesirable phenomenon include wellbore instability, lost circulation, and stuck pipes. As barite sagging is a complex phenomenon, the mechanisms that cause and aggravate it still need to be fully understood to mitigate these problems. This study examines barite sagging in the wellbore with inner pipe rotation to understand the process and develop prevention strategies. Thus, a flow loop study with OBM is conducted in a concentric annular test section with varying inner pipe rotation and inclination angles. The tests were performed at an elevated temperature (49ยฐC) to simulate borehole conditions. By measuring the pressure profile in a mud sample trapped in the test section, barite sagging was evaluated. Using the data, we calculated the density difference between the top and bottom sections of the column. The novelty of the work lies in continuous monitoring of the density profile of the mud column, which is sheared between two coaxial cylinders to simulate drillstring rotation in the wellbore, and utilizing the data for evaluating barite sag. The results show the evolution of the pressure profile with time, indicating the sagging of barite particles at the bottom of the test section. Due to barite sagging, the density of the top portion of the mud column decreased over time, while the density of the bottom part increased. The lateral sedimentation of barite particles toward the annulus outer wall enhances barite sag in inclined configurations. The sedimentation creates two suspension layers with different densities, leading to secondary flow, which enhances sagging. Hence, the primary factor driving barite sagging is inclination. An increase in inclination angle from 0ยฐ to 50ยฐ resulted in a significant (more than twofold) increase in mud density difference. Also, the rotation of the pipe delayed sagging during the early phases of the testing process (less than 20 minutes). However, it did not have much effect as the sagging progressed, resulting in approximately the same density difference in both cases (i.e., with and without rotation).
- Europe (1.00)
- North America > United States > Texas (0.70)
- Asia (0.68)
- North America > United States > Oklahoma (0.46)
- Research Report > New Finding (0.84)
- Research Report > Experimental Study (0.70)
- Well Drilling > Wellbore Design > Wellbore integrity (1.00)
- Well Drilling > Drilling Fluids and Materials > Drilling fluid selection and formulation (chemistry, properties) (1.00)
- Well Drilling > Drilling Fluids and Materials > Drilling fluid management & disposal (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
ABSTRACT The Geothermal Battery Energy Storage (GBES) concept has been proposed as a large-scale renewable energy storage method beyond short-term durations. GBES has the potential to handle the variable nature of renewable solar and wind power by allowing their excess energy to heat brine at the surface which is then injected into a high porosity sedimentary reservoir. The subsurface reservoir forms a thermal battery and stores the heated brine. A subsurface water saturated high porosity sedimentary reservoir can be an ideal long-duration storage vessel, thereby providing nearly unlimited storage capacity compared to other geologic formation types. However, efficiency and safety of the GBES operations (i.e., flow and mechanical integrity of the formation) depend on the response of the wellbore and sedimentary formations to coupled Thermo-Hydro-Mechanical (THM) or Thermo-Hydro-Mechanical-Chemical (THMC) loads induced by the injection-production-reinjection cycles. If operational parameters of GBES systems are not optimized with data unique to sedimentary formations, near wellbore formation integrity issues can lead to reduced heat output. In this paper we review potential issues related to THM behavior of a GBES system and illustrate geomechanical concepts that can be adapted to provide an uplift to GBES operations. INTRODUCTION Solar and wind energy are key in our transition to a net-zero carbon future. While abundant and cost-competitive, they are variable, intermittent and their energy supply does not match all demand needs. Therefore, the key to unlocking our shift to 100% renewable energy generation is the ability to store energy beyond short-term durations such that power and heat are available on-demand at any day. The Geothermal Battery Energy Storage (GBES) concept has been proposed as a large-scale renewable energy storage method beyond short-term durations. GBES has the potential to handle the variable nature of renewable solar and wind power by allowing their excess energy to heat brine at the surface which is then injected into a high porosity sedimentary reservoir. The subsurface reservoir in this context forms a geothermal battery and stores the heated brine. Stored brine can then be produced for power generation (e.g., Organic Rankine Cycle) when necessary and subsequently the same brine can be reinjected after a heating cycle at the surface. Figure 1 illustrates the GBES concept and the cyclic nature of the operations. Colder brine is drawn from a porous and permeable sedimentary reservoir via production well(s). Brine is then heated using excess energy from the renewable sources (e.g., solar or wind) and injected back into the same reservoir through a nearby injection well. When energy is needed, hot/cold brine is produced under sufficient pressure to remain in liquid phase and generate electricity via a geothermal power plant or used for direct heating. This cycle is repeated at various regular intervals to match the energy demand (see Green et al. 2021).
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.46)
- Energy > Renewable > Wind (1.00)
- Energy > Renewable > Solar (1.00)
- Energy > Renewable > Geothermal (1.00)
- (2 more...)
A Graphical Analysis-Based Elastoplastic Solution for Undrained Wellbore Drilling Problem in Hoek-Brown Rock Formation
Abu-Dayyeh, H. (Louisiana State University) | Wang, X. (Louisiana State University) | Chen, S. L. (Louisiana State University) | Han, Y. H. (Aramco Research Center, Aramco Americas) | Abousleiman, Y. N. (The University of Oklahoma)
ABSTRACT An analytical solution is developed for evaluating the stability of an impermeable wellbore in rock formation that obeys Hoek-Brown failure criterion in a hydrostatic stress field. The solution is derived in the framework of the graphical analysis method recently developed by Chen and Abousleiman (2022). The graphical analysis approach can effectively overcome two essential limitations that exist in the implementation of the Hoek-Brown model in the current literature, i.e., first, the mathematical difficulties involved in the treatment of corner singularity of the yield surface; and second, the intermediacy assumption for the axial stress with respect to other stresses. The graph-based solution procedure follows two steps: (1) to geometrically track the stress path trajectory laid out in the deviatoric stress plane; and (2) to formulate the constitutive equations and the radial equilibrium equation. It is found that the effective stress path corresponding to the borehole drilling process is composed of piecewise curved and straight lines in the deviatoric stress plane; it eventually terminates in the major sextant with the Lode angle ฮธ ranging from ฯ/2 to 2ฯ/3 or, on the line of ฮธ = ฯ/2, depending on the mechanical properties of the rock. The analytical solution obtained is a rigorous and complete one for the borehole drilling problem. INTRODUCTION Wellbore instability is a significant subject in the application of rock mechanics in the oil and gas industry (Santarelli et al, 1986; Yu, 2000). The study and analysis of stresses around wellbores is crucial in the reduction of monetary losses associated with this problem (Yu, 2000; Chen, 2012). Choosing the right model is essential for accurately predicting mud pressure, calculating wellbore stresses, and ensuring safe wellbore drilling (Zoback, 2007; Yu, 2000). Consequently, this can help mitigate two main problems that cause wellbore instability: borehole collapse and borehole fracturing (Santarelli et al, 1986). Estimation of the critical mud pressure is obtained by choosing a failure criterion and using a constitutive model to estimate the stresses induced around the wellbore (Al-Ajmi, 2006).
Reexamining In-Situ Stress Interpretation Using Laboratory Hydraulic Fracturing Experiments
Ye, Zhi (The University of Oklahoma) | Ghassemi, Ahmad (The University of Oklahoma)
Abstract Knowledge of in-situ stress is important for many subsurface science and engineering issues. The minimum principal stress (S3, or Shmin in most cases) is typically measured through mini-frac tests. Several methods have been suggested to interpret S3 using pressure data during the injection and/or shut-in phases of a mini-frac test. However, S3 interpreted from different methods is often inconsistent with each other and could result in large uncertainty in determining net pressure. This paper presents a series of small-scale laboratory hydraulic fracturing experiments conducted under true-triaxial compression. The injection scheme consists of a hydraulic fracturing cycle followed by a few fracture propagation cycles and several injection/falloff (DFIT) cycles. The wellbore pressure and acoustic emission (AE) activities of each cycle were concurrently measured to monitor fracture initiation, propagation, and closure during fluid injection and shut-in. The pressure data were used to interpret S3 using different hydraulic fracturing-based methods. The results illustrate that the spatial-temporal evolution of AE activities is well associated with fracture propagation. The stress interpretation results from the DFIT cycles demonstrate that fracture reopening pressure generally provides a reliable estimate of the minimum principal stress (S3). ISIP consistently provides a relatively higher estimate of S3 and can be used as an upper limit for constraining S3. Fracture closure was observed using the so-called "tangent" method in all DFIT tests. However, the "tangent" method using a signature close to the peak GdP/dG tends to significantly underestimate S3. The "compliance" method offers a relatively objective (yet still low) estimate of closure pressure. However, the signature associated with the change in system stiffness or compliance is observed but not consistently present in every DFIT cycle. It has been observed that the non-uniform fracture topography significantly impacts fracture closure behavior and the associated stress interpretation. Considering the complex nature of hydraulic fracturing in the subsurface, multiple techniques may need to be integrated for the determination of S3.
- Well Completion > Hydraulic Fracturing (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Reservoir geomechanics (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Pressure transient analysis (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Drillstem/well testing (1.00)
An Uncertainty Risk Evaluation Tool for Wellbore Leakage Prediction for Plug and Abandonement (P&A)
Ugarte, Esteban R. (The University of Oklahoma) | Salehi, Saeed (The University of Oklahoma)
Abstract Oil and gas wells leakage is a major concern due to the associated risks. Potential issues include habitat fragmentation, soil erosion, groundwater contamination, and greenhouse gas emissions released into the atmosphere. An estimated 2 million abandoned oil and gas wells are believed to be leakage. Proper Plug and Abandonment (P&A) operations are required to ensure these wells are correctly disposed of from their useful operational life. This study aims to build an uncertainty evaluation tool to statistically classify the risk of a well from leaking based on their well information (age, location, depth, completion interval, casings, and cement). Data consists of leakage reports and available well data reports from Alberta Energy Regulator (AER) in Canada. Multiple preprocessing techniques, including balancing the data, encoding, and standardization, were implemented before training. Multiple models that included Naรฏve Bayes (NB), Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN) were compared to select the best-performing for optimization. RF outperformed the other models and was tuned using hyperparameter optimization and cross-validation. The final model's average accuracy was 77.1% across all folds. Multiple evaluation metrics, including Accuracy, Confusion Matrix, Precision, Recall, and Area Under the ROC Curve (AUC), were used to assess the model and each class against the rest. Feature importance showed an even distribution across the different features used. The model presented in the study aimed to classify wells and label the leakage risk based on the well information associated with its components. This risk evaluation tool could help reduce gas emissions by 28.2% based on the results obtained. This tool can classify the wells to speed the selection process and prioritize wells with higher leakage risk to perform P&A operations and minimize emissions.
- North America > United States (1.00)
- North America > Canada > Alberta (0.35)
- North America > United States > Colorado > DJ (Denver-Julesburg) Basin > Wattenberg Field (0.99)
- North America > United States > West Virginia > Appalachian Basin > Marcellus Field > Marcellus Shale Formation (0.94)
- North America > United States > Virginia > Appalachian Basin > Marcellus Field > Marcellus Shale Formation (0.94)
- (6 more...)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Performance Analysis > Accuracy (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.67)
Study of Controlling Parameters of In-Situ CO2 EOR Using Numerical Simulations
Wu, Xingru (The University of Oklahoma) | Dai, Lei (Southwest Petroleum University) | Chang, Qiuhao (The University of Oklahoma) | Qiuhao, Sadam (United Energy Pakistan Limited) | Shiau, Bor Jier (The University of Oklahoma)
Abstract Laboratory experiments have demonstrated that injecting urea solution as a CO2-generating agent into an oil reservoir may significantly enhance oil recovery. When the reservoir temperature is above 50ยฐC, urea is hydrolyzed to carbon dioxide and ammonia. This technology overcomes many supercritical CO2 problems and can be very attractive for thousands of stripper wells that produce oil on marginal economic feasibility. However, previous efforts mainly focus on laboratory tests and mechanisms study. The actual field performance of this technology is likely dependent on reservoir heterogeneity, and its economic viability is expected to be closely related to its optimization. This highly relies on numerical modeling and simulation capability. The synergic mechanisms in in-situ CO2 EOR (ICE) using urea are complex. Firstly, the decomposition of urea injected leads to CO2 and ammonia under proper reservoir conditions. The generated CO2 in brine partitions preferably into the oil phase and decreases oil viscosity while swelling the oil effectively. The co-generated product, ammonia, can potentially reduce the interfacial tension (IFT) between the oil/water phase, which moves the relative permeability (or saturation) curves and position to offer additional oil production. In the first attempt, the dominant parameters, including urea reaction kinetics, the stoichiometry of the decomposition process, the oil swelling effect, and the impact of IFT reduction on the relative permeabilities, were considered and incorporated into the numerical modeling effort. We used the chosen numerical simulations to determine the contribution of the individual mechanism by history matching the results of laboratory tests collected previously. The one-D mechanistic numerical model was then upscaled to a synthetic homogeneous 3D model by simulating a quarter of the 5-spot sector model to evaluate the feasibility and engineering design of ICE for future field scale pilot tests and potential prize of ICE EOR. After comparing the base case with urea injection, a sensitivity analysis was performed. As part of the aims, the simulation results differentiate and reveal the incremental contributions of the synergetic behaviors among several mechanisms: oil viscosity reduction, oil swelling, and IFT reduction. Data also showed that the IFT reduction plays a rather minor role in this effort, and its contribution is basically indistinguishable. The predominant recovery mechanisms are mainly controlled by oil swelling and viscosity reduction; temperature plays a key role in influencing the extent of reaction kinetics of urea. In the 1D simulation, the temperature significantly impacted the production performance as the core cooled down quickly. In a 3D or field-scale scenario, the waterflooding does not change the in-depth reservoir temperature as the temperature gradient moves at a much slower rate (about two times slower) than the injected urea solution slug. However, the duration of water flooding should be considered for field project design as it may alter the temperature profile in the reservoir.
- North America > United States (0.47)
- Asia > Middle East > Israel > Mediterranean Sea (0.24)
- North America > Canada > Saskatchewan > Williston Basin > Bakken Shale Formation (0.99)
- North America > Canada > Manitoba > Williston Basin > Bakken Shale Formation (0.99)
- Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Waterflooding (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Chemical flooding methods (1.00)
Propagating Hydraulic Fractures from Wellbores: Effects of In-Situ Stress and Near-Wellbore Stress Redistribution
Gao, Qian (College of New Energy, Xiโan Shiyou University) | Zhou, Desheng (College of Petroleum Engineering, Xiโan Shiyou University) | Ghassemi, Ahmad (The University of Oklahoma (Corresponding author)) | Liu, Xiong (College of Petroleum Engineering, Xiโan Shiyou University) | Liu, Yafei (The University of Oklahoma) | Guo, Minhao (College of Petroleum Engineering, Xiโan Shiyou University)
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, ฯv โ ฯhmin 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.
Fault characterization in a postsalt reservoir interval, Jubarte Field (Campos Basin), using seismic attributes and machine learning
Perico, Edimar (Petroleo Brasileiro S.A. (Petrobras), The University of Oklahoma) | Bedle, Heather (The University of Oklahoma) | Buist, Bobby (The University of Oklahoma) | Damasceno, Andrea Carvalho (Petroleo Brasileiro S.A. (Petrobras))
Abstract Seismic attributes are routinely applied to interpretation tasks to improve the recognition of geologic features in the subsurface. Subtle changes in the amplitude and phase components of the seismic data can reveal details and provide information about the management of hydrocarbon reservoirs. To this end, we investigate how instantaneous and geometric attributes can help in fault and discontinuity recognition. Seismic data from Jubarteโs 4D/4C Permanent Reservoir Monitoring system, offshore Brazil, were used to test the impact of a variety of algorithms and parameters in improving fault visualization. The presence of seismic noise and geologic units with weak acoustic impedance contrasts motivated the application of additional methods such as data conditioning workflows and unsupervised machine learning methods. We found that spectral balancing and structure-oriented filtering increased the lateral continuity of some stratigraphic reflectors and attenuated the random noise, which improved fault surface visibility. Attributes were calculated on the full-stack data and azimuth-restricted volumes revealing different impacts of the seismic noise and changes in the lateral continuity of the features. In some cases, faults are better delineated when the acquisition direction is oriented perpendicular (or nearly) to the discontinuity. In addition, the most-positive and most-negative curvature components indicate more details of major features. A large number of seismic cubes and attributes motivated the use of principal component analysis and self-organizing maps, which complement the identification of fault segments with clusters composed of specific neurons aligned within structural discontinuities. The improvements obtained in terms of fault visualization demonstrate the importance of having a workflow that combines different fault and fracture identification methods. An integrated study is important as it identifies effects related to seismic noise and stratigraphic features. For the Jubarte Field, a multiattribute approach demonstrates advantages for delimiting the lateral extension of faults and a more precise discontinuity location.
- Geology > Structural Geology > Fault (1.00)
- Geology > Geological Subdiscipline > Stratigraphy (1.00)
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (0.87)
- (2 more...)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
- South America > Brazil > Espรญrito Santo > South Atlantic Ocean > Campos Basin > Block BM-C-30 > Jubarte Field (0.99)
- Oceania > Australia > Western Australia > North West Shelf > Carnarvon Basin > Carnarvon Basin > Dampier Basin > Rankin Platform > Greater Gorgon Development Area > Block WA-268-P > Greater Gorgon Field > Gorgon Field (0.99)
- Oceania > Australia > Western Australia > North West Shelf > Carnarvon Basin > Carnarvon Basin > Carnarvon Basin > Rankin Platform > Greater Gorgon Development Area > Block WA-268-P > Greater Gorgon Field > Gorgon Field (0.99)
- (9 more...)
A Robust Screening Tool to Repurpose Hydrocarbon Wells to Geothermal Wells in Oklahoma
Ugarte, Esteban R. (The University of Oklahoma) | Salehi, Saeed (The University of Oklahoma)
Abstract Geothermal energy is a renewable energy that has vast potential due to its reliable energy supply. Its development has been related to specific geological locations with extremely high temperatures. However, depleted oil and gas reservoirs can produce geothermal energy from the subsurface. Repurposing this well can be a valuable tool to generate sustainable and steady energy for the state of Oklahoma due to its large number of wells used in the Oil and Gas industry. In fact, abandoned oil and gas wells are suitable candidates for conversion as these are environmental liabilities. The challenge is selecting which wells are good candidates for geothermal applications. This study aims to build an evaluation methodology to filter wells with a high potential for geothermal production. Three factors, temperature, proximity to the end user, and well integrity, are analyzed for evaluating possible candidates. Three datasets of temperature gradients were gathered from the Oklahoma Geological Survey, abandoned oil and gas wells from the Oklahoma Corporate Commission, and citiesโ locations and populations from the US Census Bureau were combined. The objective is to evaluate the wells in Oklahoma to select promising candidates for repurposing for geothermal applications. Temperature prediction was made using Spatial Interpolation using Thiessen polygons, K-nearest Neighbors, and Kriging. K-nearest Neighbors exhibited the highest performance based on the evaluation metrics. Temperature prediction at an average true vertical depth of 6000 ft showed 26.7% or 4292 wells have more than 150 ยฐF and can be converted for geothermal production. The shortest distance heuristic algorithm was used to calculate the shortest distance of each well to any city in Oklahoma. Before conversion, an evaluation of the well is required to assess the volumes and condition of the well; methods include statical analysis, logging, and evaluation techniques. These are discussed in this study. This study shows the high number of wells with the potential to be converted for geothermal applications converting a liability and environmental concern to a renewable energy-producing asset.
- Research Report > New Finding (1.00)
- Research Report > Experimental Study (0.68)
- Government > Regional Government > North America Government > United States Government (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Energy > Renewable > Geothermal > Geothermal Energy Engineering > Geothermal Production (0.44)
- North America > United States > Oklahoma > Arkoma Basin > Cana Woodford Shale Formation (0.99)
- North America > United States > Kansas > Cherokee Basin (0.99)
- North America > United States > Arkansas > Arkoma Basin > Cana Woodford Shale Formation (0.99)
- (6 more...)
Application of Machine Learning to Evaluate the Performances of Various Downhole Centrifugal Separator Types in Oil and Gas Production Systems
Osorio Ojeda, Laura Camila (The University of Oklahoma) | Olubode, Michael (The University of Oklahoma) | Karami, Hamidreza (The University of Oklahoma) | Podio, Tony (Echometer)
Abstract Pumping artificial lift techniques, such as rod pumps and ESPs, are applied for gassy wells more than ever before. This has made the downhole separators a critical part of most such installations. There are multiple categories of downhole separators, with various techniques developed to assess and improve their performances, but no general guidelines are established for their application. This paper aims to classify the separator types and review their performances in the open literature. In addition, various data sets are collected and put together to evaluate and rank downhole centrifugal separators using data analysis and machine learning (ML) techniques. A comprehensive literature review is conducted to collect the available downhole separator performance data. Experiments and Computational Fluid Dynamic (CFD) simulations are the techniques used by the researchers. This information is collected to identify the optimum conditions for each separator type, considering the effects of liquid and gas rates and other flow parameters. The data collected from various research projects over the last 20 years are combined to make a comprehensive downhole separation databank. These data are analyzed using various machine learning algorithms to rank the performances of downhole separators at various operating conditions. Various downhole separators have been tested in the open literature, including poor-boy separators, two-stage separators, packer-type separators, rotary and spiral separators with different designs, etc. A critical factor that adds to the uncertainty is the separator's control system, which significantly affects its efficiency. The available data show that most separators provide separation efficiencies higher than 80% if the downstream casing valve is adequately controlled. The separation efficiencies decline as the liquid and gas rates increase past an upper limit. The collected data from multiple previous studies form a broad dataset. Data analysis is used to compare the performances of different downhole separator classes, and machine learning is applied to identify a robust prediction model. This paper gathers, interconnects, and examines several available research works through data analytics. The results provide a fundamental source and a valuable guideline for downhole liquid-gas separation, particularly in artificial lift applications.
- Production and Well Operations > Well Operations and Optimization > Downhole fluids separation, management and disposal (1.00)
- Facilities Design, Construction and Operation > Processing Systems and Design > Separation and treating (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)