bluuebunny/crossref_metadata_2025_split huggingface dataset:
Technical, Economic and Environmental Considerations for Selecting Next Generation Hydraulic Fracturing Equipment Technology
Authors: Fu Dan , Zemlak Warren , Yeung Tony , Barclay Caleb , Gorchynski Trevor 
Published: September 2022

Abstract
Recently, the North America Oil and Gas industry has seen a rapid increase in the adoption of new hydraulic fracturing technologies such as dual-fuel diesel engine, electric system powered by gas turbine or engine on-site and turbine direct drive technology, to reduce emissions and operating costs. The objective of this paper is to provide a detailed analysis of economic, environmental, and technical considerations when selecting the next generation hydraulic fracturing equipment platform.
It is believed that any next-generation technology must meet the following key requirements: 1. Reduction of GHG and EPA regulated emissions; 2. Reduced equipment footprint; 3. Capable of meeting the most stringent noise standard; 4. Improved reliability; 5. Improved pad-to-pad mobility; 6. Reduced maintenance and personnel costs; 7. Competitive capital cost. For the selection process, a methodology was developed to evaluate the energy density of fuel, thermal efficiency of prime movers, mechanical power transfer efficiency, and equipment operating environment and configuration against the above objectives. The methodology also considered the technical and commercial feasibility of key components.
Natural gas is selected as the mobile primary energy source due to its higher energy density and lower emission profile than conventional diesel, and more economical and widely available on-site. Among all available natural gas-powered engines evaluated, which included dual-fuel diesel engine, gas reciprocating engine, single large turbine and direct drive turbine, the direct drive turbine scored the highest. The direct drive pumping unit is equipped with a 5,000 HHP continuous duty power end driven by a 5,000 HHP dual shaft turbine through a single speed reduction gearbox. This combination provides the most efficient mechanical power transfer efficiency resulting in significant fuel cost savings and reduction in greenhouse gas emissions. Because of its high-power density, the direct drive turbine system can potentially reduce the number of on-site equipment by 43% and personnel by 31%. Comparing to other next generation hydraulic fracturing system, the direct drive turbine technology has the lowest capital cost per HHP.
Gas Transport Modeling in Organic-Rich Nanoporous Media with Nonequilibrium Sorption Kinetics
Authors: Liu Zizhong , Emami-Meybodi Hamid 
Published: September 2022

Abstract
We present a mathematical model for one-dimensional gas transport in organic-rich nanoporous media subject to nonequilibrium sorption. The model is developed from two governing equations to simulate Knudsen diffusion and viscous flow in the free phase, and surface diffusion in the sorbed phase. The pore space is shared between the free and sorbed phases by defining concentration-dependent free- and sorbed-phase volume fractions. The governing equations are coupled through a source/sink term described by a kinetic sorption model. The impact of the reduced effective pore space and sorption on free-phase mass transfer is characterized by defining effective diffusion coefficients. The governing equations are numerically solved based on the finite element method. The diffusion model is utilized to analyze the temporal and spatial concentration data obtained using X-ray micro-CT scans from two experiments, including Krypton transport into a coal sample and Xenon uptake into a shale sample. The proposed model can closely reproduce total concentration profiles in both experiments. The model also captures the concentration peak in Xe-shale system due to the significant nonequilibrium sorption and slower process of reaching equilibrium. The results show that surface diffusion dominates the total mass transport in Xe-shale system with higher adsorption affinity. In Kr-coal system with lower adsorption affinity, the sorbed phase contributes significantly to the total mass transport mostly at high pressures. In addition, the sorbed phase can occupy up to 30% of pore space, which reduces the free-phase diffusion coefficient by 40% in Xe-shale and 80% in Kr-coal. Accordingly, neglecting the sorbed-phase volume in nanoporous media may overestimate the effective free-phase diffusion coefficient.
Case Study: Plunger Lift Optimization Leveraging Physics-Assisted AI and the Impact on Greenhouse Gas Emissions Reductions
Authors: Olszak Evan , Yoho Michael , Greaser Ralph , Srivastava Ravi , Lanke Ramesh , Moffett Ross , Chan Chun Sik , Dabrowski Dariusz , Pond Burke , Hingerl Ferdinand 
Published: September 2022

Natural gas has been playing an increasingly important role in the global energy transition with various efforts underway to minimize greenhouse gas emissions during gas production to align natural gas E&P operations with strategic ESG goals. Plunger lift (PL), one of the major artificial lift types applied to natural gas wells, leverages the potential energy naturally present in gas wells to lift liquid to the surface and hence has a lower operational greenhouse gas emissions footprint than other lift types leveraging pumps (ESP), compressors (Gas Lift) or electric motors (Rod Lift). A common challenge in PL wells is the occurrence of liquid loading events, during which the mass of the liquid column and plunger becomes too large to be naturally lifted, and manual intervention techniques are required to bring the plunger and liquid back to the surface. One such intervention technique is the venting of pressurized gas from the well. During this process, very large amounts of methane and other pollutants are released into the atmosphere, which not only presents a significant economic loss but also results in a substantial negative impact on the greenhouse gas footprint of the PL operation. This study presents results from a field pilot during which physics and AI-assisted technology are leveraged to automatically optimize plunger lift operations with the objective of minimizing flowing bottom hole pressure, decreasing the variance of plunger velocities, and avoiding plunger rise velocities that are outside safe operating standards. During the presented field pilot, production of natural gas was increased by 4% while the number of required venting events and the associated release of greenhouse gas was reduced by 48% due to enhanced well stability.

Novel/Additive Information
Fully automated setpoint management leveraging physics-assisted artificial intelligence algorithms was implemented in a field-based pilot, which resulted in a monthly production uplift up to 4% over plan as well as a 48% decrease in greenhouse gas emissions due to increased well stability and fewer venting events.
Design and Development of a High Pressure-High Temperature Wellbore Simulator for Investigation of the Impact of Cyclic Stresses on the Integrity of Wellbore Sections with Cement/Casing Interfaces
Authors: Lin Zichao , Kuru Ergun , Iremonger Simon , Yang Xinxiang , Fisher Blair , Spence Jeff 
Published: September 2022

Abstract
Understanding the mechanisms of leakage-pathway formation are crucial for proper assessment, remediation, and in the end, prevention of leaking wells due to severe modern-day well stresses such as multistage hydraulic fracturing operations. Aiming to improve the understanding of the drivers behind fluid leakage from wells subject to modern-day well stresses, an experimental study of the cement sheath integrity under downhole stress conditions was conducted using a custom-designed and fabricated physical wellbore simulator. The physical wellbore simulator was designed and constructed to be capable of measuring the permeability (up to nanodarcy level) of the cemented annulus between two casings under cyclic temperature and pressure conditions (up to 43 MPa and 120°C).
Permeability (nitrogen) of the casing-cement-casing system was measured under i-) non-cyclic temperature and pressure; ii-) non-cyclic temperature and cyclic pressure; iii-) cyclic temperature and non-cyclic pressure conditions. Potential leakage pathways were visually inspected after each experiment. The rough shear bonding strength between the inner casing and the cement sheath was also measured.
Three factors were identified as having the most significant impacts on the permeability of the cemented annulus between two casings: i). Cement and/or casing shrinkage/expansion caused by the temperature change, ii). Casing shrinkage/expansion caused by the inner casing pressure change and, iii). Test duration time (time after curing and before each permeability measurement). The final permeability of the cemented annulus was controlled by the combined effects of these three factors. Overall, cement was extremely resilient to stresses, and slight increases in permeability were only observed after subjecting the cement sheath to significant stress. Once the debonding occurred at the cement/casing interface due to the initial change in pressure and/or temperature, applying cyclic pressure load did not significantly alter the permeability of the cemented wellbore section. The shear bonding strengths were on the high side, and the final permeabilities were below or around the critical permeability of 0.1 mD (Ozyurtkan et al. 2013).
Through the development of the physical wellbore simulator the factors affecting the integrity of the cement-casing interfaces under representative real-world wellbore conditions of variable temperature and pressure conditions have been investigated. The improved understanding of the factors contributing to leakage-pathway formation are now being used to implement more reliable barrier technologies for effective mitigation of fugitive emissions.
RTA-Assisted Numerical History-Matching and Co-Optimization of CO2 Storage and HnP Performance for a Near Critical Gas Condensate Shale Well
Authors: Hamdi Hamidreza , Clarkson Christopher R. , Ghanizadeh Amin 
Published: September 2022

Abstract
Due to strong non-linearities in the diffusivity equation, numerically-assisted rate-transient analysis (RTA) techniques have been suggested for analysis of multi-phase production data from multi-fractured horizontal wells (MFHWs). However, these methods are based on some limiting assumptions that cannot be generalized for three-phase flow or when relative permeability is unknown. In this study, a new RTA-assisted history-matching technique is proposed to simultaneously match production data and diagnostic plots during the calibration process.
In the proposed method, the objective function is modified to include the derivative of the integral of rate-normalized pressure for the primary phases. As such, in the history-matching process using compositional numerical simulation, the flow regimes are also matched, which can increase the reliability of the calibrated numerical model. This approach is applied to a challenging dataset: production data from a MFHW completed in a Canadian shale reservoir hosting a near-critical gas condensate fluid. The calibrated model is then applied to co-optimize CO2 storage and oil production using a cyclic gas injection scheme.
The results demonstrate that when the modified objective function is used, the history-matching scheme will reject models that cannot reproduce the flow regimes even if the production data are visually matched. Another benefit of this modified history-matching workflow is that, unlike other numerically-assisted RTA techniques, it is not limited to any specific conceptual model or reservoir geometry. Further, interactions between parameters are accounted for during the calibration process. Co-optimization using the calibrated model leads to an optimized Huff-n-Puff (HnP) design that can produce 40% additional (incremental) oil, while around 17% of the injected CO2 is stored during the cyclic CO2 injection process.
In this study, a modified objective function is introduced for the first time to enhance the numerical history-matching process to ensure the resulting calibrated model can also reproduce the observed transient flow regimes. This approach is easy to implement and is not limited to a specific model geometry or any input-output relationship.
Economic Yardsticks for the End of Economic Life: Holdback and Its Adjuncts
Authors: Purvis D. 
Published: September 2022

Abstract
Traditional economic measures use chiefly net present value to compare on-going net revenue to plugging liabilities and other retirement liabilities, but operators onshore in the US plan to pay for plugging costs from cash flow. Liabilities have accumulated and continue to accumulate as operators defer plugging. At the same time, production declines, and cash flow tapers and becomes riskier.
When retirement liabilities become significant compared to thinned cash flow, traditional yardsticks obscure the risk of ultimate insolvency. Future cash flow becomes insufficient to fund liabilities years before the present value flags the inversion. Recognizing the cash shortfall – which can occur surprisingly early when liabilities are allowed to accumulate – can fundamentally change the way the investment is viewed, valued, and managed.
Planning, therefore, requires new economic yardsticks to characterize the nature of cash flows with large retirement obligations, what can be called "holdback" and its adjuncts. These yardsticks look differently at the timing and risk of asset retirement obligations, namely backward from the end of the projection. "Holdback" is analogous to payout but in reverse from the end of economic life. Related measures similarly use other conventional yardstick concepts in reverse to characterize the situation more fully and to help an operator avoid orphaning its abandonment liabilities to be paid by taxpayers.
Demonstration of Autonomous Drilling on a Full-Scale Test Rig
Authors: Mihai Rodica , Cayeux Eric , Daireaux Benoît , Carlsen Liv , Ambrus Adrian , Simensen Per , Welmer Morten , Jackson Matthew 
Published: September 2022

Abstract
During recent years there has been an increased focus on automating drilling operations and several solutions are in daily use. We describe here results and lessons learned from testing on a full-scale test rig, the next step in drilling automation, namely autonomous drilling. By autonomous drilling we mean a system capable of taking its own decisions by evaluating the current conditions and adapting to them while considering multiple horizon strategies to fulfill the drilling operation goal.
Autonomous drilling was demonstrated during a series of experiments at a full-scale test rig in Norway. The focus of the experiments was to reach the target depth as quickly and as safely as possible. Since the formation at the test rig is very hard, a previously drilled well was filled with weak cement of variable strengths to allow for fast drilling. As part of the experiments, it was planned to have drilling incidents to test the system capabilities in managing arising issues and recover from them. During the experiments no real-time downhole measurements were available, only surface data.
In total 500 meters have been drilled in autonomous mode. The autonomous system is built as a hierarchical control system containing layers of protection for the machines, well and the commands, in addition to recovery procedures, optimization of the rate of penetration and autonomous decision-making. The system continuously evaluates the current situation and by balancing estimated risks and performance, e.g. risk of pack-off versus prognosed time to reach the target depth, decides the best action to perform next. The autonomous decision-making system is tightly connected with the control of the drilling machines and therefore it executes the necessary commands to follow up the computed decision. Drilling incidents may occur at any time and an autonomous system needs to be able to adapt to the current situation, such that it can manage drilling incidents by itself and recover from them, when possible. During the experiments, several drilling incidents occurred, and the system reacted as expected.
Surface data, together with internally computed data from the autonomous decision-making algorithms, were logged during the experiments. Memory-based downhole data was available after the experiments were concluded. Based on all the data collected, an analysis of the behavior of the system was performed after the experiments.
During the drilling experiments at the full-scale rig, the autonomous system adapted its decisions to the surrounding environment and tackled both smooth drilling situations and drilling incidents. To cope with possible lower situational awareness, the autonomous system manages by itself transitions from autonomous to manual mode if necessary. This feature, together with fault detection and isolation capabilities, are crucial for safe operation of an autonomous system.
Polymer Augmented Low Salinity Flooding in Low Permeability Carbonate Reservoirs
Authors: Haofeng Song , Pinaki Ghosh , Annalise Bowers , Kishore Mohanty 
Published: September 2022

Abstract
Low salinity waterflooding improves displacement efficiency in initially oil-wet reservoirs if it can alter wettability, but it is often a slow process. Polymer flooding usually does not improve displacement efficiency, but enhances sweep efficiency. In this work, the synergy between low salinity and polymer flooding is studied for low permeability carbonate rocks. Polymer solutions were consecutively filtered through a 1.2 µm mixed cellulose ester membrane and a 0.4 µm polycarbonate membrane. With the proper preparation method, two polymers (HPAM and AN132) with the molecular weight of 6 MDa were successfully injected into the oil-aged carbonate rocks with the absolute permeability of 10-20 mD. Low salinity polymer flood was carried out using HPAM prepared in diluted seawaters (with modified sulfate concentrations). After extensive water floods, HPAM prepared in the 10 times-diluted seawater produced the same incremental oil recovery (4-5% original oil in place) as the ATBS-polymer AN132 prepared in the seawater. Increasing the sulfate concentration by four- and eight-folds doubled the incremental oil from low salinity polymer floods.
Investigation of Corrosion Inhibitor Actives for Anti-Agglomerant Low Dose Hydrate Inhibitor/Corrosion Inhibitor (AA/CI) Combo Products
Authors: Bartels Jeremy , Rana Geeta , Sharma Pritesh , Moloney Jeremy 
Published: September 2022

Objectives/Scope
Anti-agglomerant low dose hydrate inhibitors (AA-LDHIs) have the potential to exhibit corrosion inhibition due to their phase-boundary association and surfactant properties as well as containing similar functional groups as corrosion inhibitors (CI). However, it is often found that AA-LDHI molecules, on their own, do not possess the corrosion inhibition efficacy to meet commonly used industry standard corrosion rate criteria of less than 4 mpy general corrosion and minimal localized corrosion, even when dosed at typical AA-LDHI treatment rates (at the percentage level). For offshore applications, where an operator may have limited umbilicals for production chemical delivery and are conscious of capital expenditure, a combination chemical product would be the preferred option. In the search for such a single umbilical combination product to provide both corrosion and hydrate protection, a highly effective AA-LDHI/CI combination product was formulated.

Methods, Procedures, Process
The newly developed AA-LDHI/CI product was first evaluated in the laboratory under field simulated environments: rotating cage autoclave (RCA) to test corrosion inhibitor performance and visual sapphire hydrate rocking cells to test anti-agglomerant performance before being taken to the field.

Results, Observations, Conclusions
The corrosion inhibitor performance testing was conducted under sweet conditions using RCA at a temperature of 158 °F and a shear rate of 40 pascals. The post-test coupons were further evaluated for pitting corrosion performance using Vertical Scan Interferometer and coupon surface features were measured. After a 7-day test, the AA-LDHI/CI reduced the general corrosion rate to &lt;4mpy whilst localized corrosion features were no greater than what was already present on the surface before exposure. The product was also tested for AA-LDHI performance utilizing visual sapphire hydrate rocking cells at 2,000 psi and 40 °F under shut-in/restart conditions which showed no impacts on performance when compared with the AA-LDHI chemistry by itself at the same dosage.

Novel/Additive Information
The paper describes the development and laboratory testing of new AA-LDHI/CI combination products that showed exceptional corrosion inhibition performance while also maintaining hydrate inhibition metrics with no change to secondary properties and with positive sustainability impacts. After laboratory development and testing, the new product was then field deployed. Preliminary data from an ongoing field trial is also shared.
Flowing Bottomhole Pressure Prediction with Machine Learning Algorithms for Horizontal Wells
Authors: Baki Sohrat , Dursun Serkan 
Published: September 2022

Abstract
Flowing Bottom Hole Pressure (FBHP) is one of the critical parameters during evaluation of unconventional oil and gas horizontal wells. The FBHP is mainly used for production optimization, calculation of productivity index and assessment of well performance evaluation with respect to offset wells. To calculate FBHP, production engineers use physics-based models and empirical correlations calibrated with actual physical measurements from offset wells. The objective of the paper is to develop a data driven approach for prediction of the FBHP with machine learning algorithms during post frac flowback period with multi-phase flow regimes. Surface production data from 30+ wells were collected with the key attributes such as wellhead pressure, gas rate, condensate rate, water rate, wellhead temperature, choke size, fluid density. FBHP was calculated based on physics-based software calibrated with physical downhole pressure gauges. In machine learning workflow, data collection and cleaning, exploratory data analysis, train-test split, pre-processing, cross-validation and tuning tasks were employed to obtain the accurate and generalized predictive models. Ensemble tree-based, support vector machines, and neural network models were explored in this study and their performances were evaluated by several regression error metrics. Analysis of model results showed the potential of machine learning approach for predicting the FBHP especially in multiphase flow regime. The model results showed accurate prediction of the FBHP within acceptable confidence interval application period. It is important to note that model accuracy dependents on testing period and flow condition as expected from interpolation nature of machine learning predictive models. The models were applied to new wells which were not used in the training process. The prediction results on the new wells showed outstanding performance and thus enabled decision making for wide area of application. This study shows that machine learning based predictive models can learn the patterns in complex parameters governing the FBHP behavior. Furthermore, robust machine learning models can be used for different phase of production period to optimize field development.