bluuebunny/crossref_metadata_2025_split huggingface dataset:
Life-Cycle Gradient-Based Production Optimization Including Well-Shutoff Option with Least-Squares Support Vector Regression
Authors: Almasov Azad , Onur Mustafa 
Published: October 2023

Abstract
The objective of this work is to present an efficient method based on gradient-based optimization using a least-squares support-vector regression (LS-SVR) model to solve well-shutoff and well-control optimization problems. We formulate a continuous differentiable NPV for the well shutoff optimization problem. In our approach, switching well on/off times are considered part of the design variables. Our parameterization is based on a fixed number of cycles, the length of each cycle, and the production time fraction in each cycle. The remaining fraction of each cycle is the shutoff time fraction. We use linear equality constraints so that the summation of the length of each cycle is equal to the life of the production, and thus, we do not need to truncate the length of the last cycle. We consider both the stochastic simplex gradient optimization and the machine learning-based least-squares support vector regression (LS-SVR) proxy but we update it during optimization so that the updated proxy remains predictive toward promising regions of search space during the optimization. We compare the performance of the proposed method using the LS-SVR runs coupled with iterative sampling refinement method (ISR) to update the proxy during optimization with the popular stochastic simplex approximate gradient (StoSAG) and reservoir- simulations runs for a synthetic example considering a waterflooding process in a conventional compositional oil reservoir with 2 water injectors and 4 producers. Results show that higher computational efficiency is achieved using the LS-SVR-based optimization method over the StoSAG-based optimization method using a high-fidelity numerical simulator. The proposed LS-SVR-based framework is shown to be at least 3 to 7 times computationally more efficient, depending on the cases considered than the StoSAG using a high-fidelity numerical simulator. For the waterflooding optimization example, designing multiple shutoffs and making cycle length unknown are found to be not beneficial as compared to single shutoff cases as they yield lower NPVs than single shutoff cases. However, we observe that the size and sampling of the training data, as well as the selection of bound constraints for the well controls, influence the performance of the LS-SVR-based optimization method. The well-shutoff/well-control optimization problem can be handled with the gradient-based optimization methods by introducing a production time fraction as the design variable for each cycle. This is the first LSSVR application for the well shutoff and well-control optimization problem. The proposed LS-SVR-based optimization framework has great potential to be used as an efficient tool for this type of optimization problem.
Review of Downhole Gas Liquid Separators In Unconventional Reservoirs
Authors: Al Munif E. H. , Alhamad L. , Ejim C. E. , Banjar H. M. 
Published: October 2023

Abstract
The accumulation of liquid in deeper wells poses a critical problem as it significantly reduces the well's productivity index. One of the methods used to lift the accumulated liquid is the sucker rod pump system (SRP). However, lifting large volumes of liquid and associated gas to the surface artificially has been challenging, particularly with rod pump systems. To address this issue, a downhole gas separator can effectively be deployed below the pump intake to separate the free gas from the produced liquid. The gas separated downhole can then be extracted through the tubing-casing annulus while the liquid is artificially lifted through the tubing. The paper endeavors to provide a comprehensive review of recent advancements, technologies, and challenges related to downhole gas-liquid separators. The findings of this study can serve as a valuable guide for the development of downhole gas-liquid separation technologies in the industry, particularly for installation in unconventional wells.
This review includes various laboratory evaluation tests and field examples that outline the efficiency and reliability of some downhole gas-liquid separators. There are two approaches implemented to design separators. The first approach is called static gas separation, based on the gravity principle. The second approach is dynamic gas separation, which is based on applying centrifugal forces through rotational speed. However, several downhole gas-liquid separators have low efficiency and lack an acceptable guideline for their optimum design. In some fields that suffer from liquid loading problems, it may be imperative to design and install an SRP and a downhole gas-liquid separator, to prevent gas lock problems.
Based on the reviewed literatures, it was shown that centrifugal separators had better gas/liquid separation efficiency comparing to gravitational separators. Cyclone centrifugal separators consistently exhibit separation efficiencies ranging from 90% to 98%, whereas gravity-based separators typically achieve efficiency levels between 70% and 90%, depending on the design and operational variables. Centrifugal separators consistently deliver exceptional separation efficiencies, with effectiveness ranging from 90% to 99%. Moreover, the swirl tubes have showcased an approximate separation efficiency of 90% and effectively handle the fluctuating gas flow rates encountered in the well.
This review comprehensively examines the advancements, limitations, and applications of downhole gas-liquid separators in oil and gas operations, specifically in conjunction with artificial lift systems. The paper aims to bridge the gap and differentiate between different types of downhole separators, offering researchers an extensive guide for their current and future investigations. Additionally, it proposes suitable technologies that can be deployed alongside the sucker rod pump (SRP) to enhance its efficiency in wells facing challenges related to liquid loading.
Best Practices for Building a Modern and Resilient Supply Chain Framework in the Oil and Gas Industry
Authors: Sharma Akash , McDonald Dan 
Published: October 2023

Abstract
The oil and gas industry operates in a complex and challenging environment with significant supply chain management risks. This paper aims to provide a comprehensive review of these risks and proposes strategies to create a best-in-class supply chain practice using Industry 4.0 technologies. With energy prices being highly volatile and the macroeconomic situation created by COVID-19, having a resilient and robust supply chain practice is essential for oil and gas operators. The paper emphasizes the need for the energy industry to shift its view of the supply chain from a tactical, reactive organization to one of strategic value.
To establish a best-in-class supply chain practice, the paper discusses the integration of various Industry 4.0 technologies, such as artificial intelligence, robotics, and automated systems. The authors suggest leveraging tools such as spend analytics and data modeling to make informed decisions and automate dispatch processes, leading to positive fiscal and ESG impacts. They also emphasize the critical role of automation of the payables process and real-time visibility of validated invoice line-item cost detail data to achieve a should-cost modeling environment. The authors propose a manage-by-exception model that focuses on operations, value creation, process improvement, and automation of redundant support functions to mitigate risk and provide a competitive advantage for the entire enterprise. The paper presents novel and additive ideas for modernizing the oil and gas supply chain.
Sand Detection Using Acoustic Sand Monitors for Liquid-Dominated Multiphase Flow Production
Authors: Nadeem Asad , Vieira Ronald E. , Shirazi Siamack A. 
Published: October 2023

Abstract
Solid particle erosion in the oil and natural gas industry can be damaging to pipes fittings and equipment, which can lead to maintenance or in the worst case a production shutdown. In both cases, this represents a huge economic loss for the industry. Acoustic sand monitoring is one of the most widely used practices to estimate the amount of sand in the flow. In addition to being commercially available, they can be easily clamped to the outside of a pipe wall, measuring the acoustic energy generated by sand grain impacts on the inner side of a pipe wall. In this work, a broad range of multiphase operating conditions has been experimentally investigated with acoustic sand monitors in large-scale flow loop facilities to determine the effectiveness of sand monitoring in liquid-dominated multiphase flow. The main objective is to use acoustic sand monitors to determine the Threshold Sand Rate (TSR). This is the minimum sand rate necessary to achieve monitor output higher than the background noise level. Acoustic monitors were placed upstream and downstream of standard (r/D = 1.5) elbows while varying superficial gas and liquid velocities, sand size (25, 75, 150, 300 and 600 micron), pipe diameter (2-inch and 3-inch) in vertical orientation. The experimental conditions were selected to cover slug-churn, dispersed-bubble flow and liquid-sand flow conditions. These results are compared to the previously obtained test results on 50.8 mm (2-inch) ID test loops.
The TSR results for liquid-dominated multiphase flow regimes are determined experimentally. The effects of sand size and flow regimes on TSR had been obtained. In 50.8 mm (2-inch) threshold limits were observed for dispersed-bubble flows regimes and liquid-sandas compared to gas-dominated flow conditions. While TSR is highest in dispersed-bubble flows regimes. The present data was compared to previous results obtained for annular, slug and stratified flow patterns in vertical flow. It was observed that the annular flow regime has the lowest TSR values showing an increase in TSR when the liquid rate increased. The results from this work can help operators understand how acoustic monitors can detect and distinguish sand impact noise from the background flow noise in liquid-dominated multiphase flow in production facilities. In this way, greater assurance is provided to operators for optimizing oil and gas production rates, especially in wells that tend to produce solids.
FluidMagic: A Sophisticated Allocation Solution for Tackling Production Technology Challenges in the Gina Krog Field
Authors: Castillo J. Reyes , Uleberg K. 
Published: October 2023

Abstract
This publication is aimed to show the potentiality of FluidMagic, an Equinor in-house developed software tool, for analysis, decision making, and production follow up. The tool was proven to be successful in differentiating from the gas in solution, gas condensate and injection gas produced by wells, and at the same time, it was able to provide estimates of the proportion of free reservoir oil produced in the Gina Krog field in the Norwegian Continental Shelf. Developed in Python, this innovative software tool is powered by a sophisticated regression algorithm that recombines and modifies the reservoir fluids to match reported test separator volumes and densities. Individual well downhole pressure and temperature, as well as fluid rates from test separator and measured gas-lift rates are used as input. FluidMagic outcomes were validated by a compositional reservoir numerical simulation model used to generate synthetic test separator data. For this purpose, the gas tracer option was enabled to track the produced injection gas fraction. Results showed a perfect conversion from test separator rates to process rates, along with a near perfect estimation of well stream flowing compositions. In addition, remarkably accurate allocation of reservoir fluid and injection gas production was achieved.
Early gas breakthrough in Gina Krog oil wells had been observed just months after production start-up and even before gas injection started. This created challenges when it comes to computing the amount of gas being recirculated throughout the oil producers with high GOR. Estimating recycled gas amounts became crucial to maximize the free reservoir oil production from the oil rim and liquid production from the gas cap. FluidMagic then became the key enabler to utilize the massive amount of data collected daily from the test separator by allowing the quantification of the corresponding allocated surface oil and gas production from well stream. Implementation of FluidMagic in Gina Krog field have proven to be effective for: quantification of the injected dry gas volumes that had been back produced by specific wells in the field, in the determination of individual well stream composition, for evaluation of the well intervention (zone isolation) efficiency as well as for gas tracer breakthrough timing confirmation. This novel approach provides the ability to accurately allocate production at reservoir and surface level virtually at real-time based on daily production data from test separator, avoiding frequent updates to the rather complex and time-consuming compositional reservoir numerical model. Similarly, this methodology minimizes the need for frequent fluid sampling in connection to feed composition predictions.
Surveillance, Analysis, and Optimization (SA&O) During Active Drilling Campaign
Authors: Zhang Yanfen , Bovet Paul , Samano Lorelea , Isabu Ozzy , Chima Andres , Everson Erik , Sun Kai 
Published: October 2023

Abstract
Deepwater drilling is an expensive complex operation. Real-time surveillance data and analysis for drilling operations are very important for ensuring safety and cost control. Due to the high production rate and high expense of deepwater wells, there are usually not many wells planned for developing a deepwater field. Therefore, the results of each well hold particular significance as additional reservoir surveillance data and are crucial for optimizing field development and production forecasts.
The subject field of this paper is WRB (pseudonym) which is a deepwater field located in the Gulf of Mexico. In the past few years, about one to two new wells per year came online at WRB field. Thus, there has been a constant stream of surveillance data from both drilling new wells and production/injection at existing wells. All the surveillance data were processed and utilized for updating the reservoir simulation model that ultimately serves as the engine for optimizing the future well locations.
This paper is intended to review and share the key learnings and best practices of Surveillance, Analysis and Optimization (SA&O) during the active drilling campaign of WRB field. A comprehensive effort was undertaken to review the historic surveillance activities carried out during drilling and post-drill, and to review the consequent value-adding decisions from effective use of surveillance information.
Thermodynamic Modeling of Aqueous Nanobubble Dispersion
Authors: Achour Sofiane H. , Sheng Kai , Lawal Tesleem , Okuno Ryosuke 
Published: October 2023

Abstract
The amount of gaseous species in water or brine can be greatly enhanced in the form of nanobubble (NB) dispersion. Aqueous NB dispersion has vast industrial applications, potentially in enhanced oil recovery and carbon dioxide (CO2) sequestration to control the mobility of gaseous species. Development of such NB technologies depends on a proper understanding of thermodynamic properties of aqueous NB dispersion. The objectives of this research are to analyze the thermodynamic stability of aqueous NB dispersion and to apply a thermodynamic equilibrium model to analyze experimental data.
We first present a thermodynamic formulation for modeling aqueous NB dispersion, which clarifies that aqueous NB dispersion occurs in the aqueous phase that is supersaturated by the gaseous species in the system. That is, the gaseous species are present in two modes: dispersion of gas bubbles under capillary pressure, and molecule dispersion (supersaturation) in the external aqueous phase. Such a thermodynamic system is referred to as aqueous NB fluid in this research, and specified by (NC + 3) variables (e.g., temperature, total volume, components’ mole numbers, and capillary pressure), in which NC is the number of components. We then present a novel implementation of the GERG-2008 equation of state (EOS) in minimization of the Helmholtz free energy to solve for equilibrium properties of aqueous NB fluid. GERG-2008 was used in this research because it is suitable for modeling an aqueous phase that is supersaturated by gaseous species.
The thermodynamic equilibrium model was applied to experimental data of aqueous NB fluid with nitrogen (N2) at pressures up to 277 bara (4019 psia) and 295.15 K (71.6°F). Application of the model to experimental data indicates that a large fraction (0.8 – 0.9) of the total amount of N2 is in the form of molecule dispersion, but such supersaturation of the aqueous phase is possible because of the presence of NB dispersion with capillary pressure. That is, NB dispersion can increase the gas content in aqueous NB fluid by enabling gas supersaturation in the aqueous phase as a thermodynamic system. Although experimental uncertainties resulted in a possible range of equilibrium properties for aqueous NB fluids at high pressures, the extrapolation of the calculation results to atmospheric pressure yielded a radius and a number density of bubbles within the range of data reported in the literature.
Casing Design Load Case for Shut-In After Worst Case Discharge Using a Progressive Depth Analysis Approach
Authors: Howard J. A. , Grant L. , D'Amico R. , McSpadden A. R. , Kalil I. A. , Trevisan R. , Glover S. 
Published: October 2023

Abstract
The US Bureau of Ocean Energy Management (BOEM) defines Worst Case Discharge (WCD) as the maximum foreseeable daily flow of oil from a well, commonly referred to as a "well blowout." When casing design engineers refer to "WCD", they focus on the impact of shutting in a maximum flow case on casing stresses as opposed to "volume" of discharge. This is sometimes referred to as Worst Case Discharge Pressure (WCDP) or Maximum Discharge Casing Pressure (MDCP) or Worst Case Discharge Load (WCDL). The US Bureau of Safety and Environmental Enforcement (BSEE) may refer to this pressure as Pressure Gradient (PG). Some international operators refer to "WCD" only as a load case and not a discharge volume so the term "WCD" may mean different things to different people. A new approach is presented to augment casing design load methodology associated with shut-in of an openhole flow event and where multiple hydrocarbon zones are exposed in an openhole section.
The proposed methodology determines the casing design loads for the highest potential flowing temperature and highest shut-in at capping pressure case establishes stresses on the existing casing and verification of casing hanger lift-off. Uniform understanding and resolution of volume-based assessment and well survivability considerations in current practice are discussed. The Progressive Depth Approach focuses on determination of the highest stress by considering the progressive contribution of multiple reservoirs flowing during an uncontrol flow event and subsequently shut-in.
The load case is conceptually structured in two events: 1) uncontrolled hydrocarbon flow through exposed casing strings with related discharge to ambient conditions (seabed in subsea wells) and 2) shut-in of the well with capping installed at wellhead. From a casing design and well integrity viewpoint, the load case enables quantification of stress changes along existing casing strings and at the casing hanger to assess well survivability. Thermal effects typically generate compressive forces (hot cases) and induce annular fluid expansion in casing annuli (if sealed) potentially leading to hot casing collapse scenarios in combination with severe draw-down pressures in the inner wellbore. Axial forces generated at the casing hanger need careful evaluation relative to lockdown mechanism ratings in a subsea wellhead.
Operators may incorrectly assume that shut-in of the deepest reservoir in an open-hole section generates the highest stress casing load. Rather it could occur from a shallower reservoir depending on formation pressures, fluid composition and contribution of the remaining reservoir exposed (commingled fluid and flow conditions). An advanced load methodology to apply flow conditions and shut-in of an open-hole flow event given multiple reservoir zones for the determination of maximum thermal effect and shut-in pressure at capping wellhead condition is presented herein.
The Secrets to Successful Deployment of AI Drilling Advisory Systems at a Rig Site: A Case Study
Authors: Behounek Michael , Ashok Pradeepkumar 
Published: October 2023

Abstract
Developing artificial intelligence (AI)-based drilling advisory software is generally straightforward when good quality labeled data are available. However, deploying such systems in the field for use by a rig crew requires careful planning and execution and often fails to provide the value proposed. It is statistically estimated that most AI projects fail, and that most companies that trial AI solutions report minimal to no impact from AI. This paper details the successful deployment and ongoing success of an AI-based drilling advisory system on rigs across an Operator's fleet, as well as deployment decisions that helped make it a high-value, sustainable, and successful program.
The Operator developing and deploying the AI system focused on five main aspects to anchor this project: setting a realistic long-term vision for automation, choosing the right tools and techniques, implementing a targeted change management plan, careful selection of team members, and planning for sustained management support. For the longer-term automation vision, decisions on where to deploy the AI models - at the rig or managed from the central office, what parts of the solution to develop in-house or out-source to achieve cost objectives, and how soon to scale AI to all rigs in the fleet were key. Finally, a thoughtful change management plan was implemented taking into consideration the company culture and industry best practices.
The project launched in 2015, with the decision to deploy the AI models at the edge/rig site with an ability to push updates from a remote, central support group as needed. The AI model platform was outsourced; AI models were developed /validated one model at a time, and then deployed to all the rigs as soon as possible. The platform and models were modularized to enable rapid prototyping, field deployment, and iterative change. A key Program Sponsor along with other Stakeholders were identified for each rig, and carefully managed to ensure ongoing support, successful adoption, and regular feedback. Transparency on how the model performed calculations was shared readily to ensure acceptance of the results by the drilling engineers and the rig site crew. An agile development and deployment cycle was adopted to maintain rig crew interest to continuously use and improve the system. Over the past eight (8) years, more than ten (10) AI models have been added incrementally to the rig-based system, which has enabled a 10% improvement in drilling performance year over year.
This paper details the decisions and processes that resulted in the successful deployment of an AI-based drilling advisory system for rigs in North America and Europe. The learning and insights from this multi-year (8 years and ongoing) deployment should provide valuable insights to those planning to deploy AI software at scale, at the edge.