bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrated Oil Companies and the Quest for Energy Transition
Authors: Alsuwailem Majed , Williams-Rioux Bertrand 
Published: September 2022

Abstract
Integrated oil and gas (O&G) companies operate vertically integrated businesses that span the O&G value chain. In this way, they can leverage expertise across multiple domains rather than just focusing on exploration and production (E&P). Examples of such companies include British Petroleum (BP), Cenovus Energy, Chevron, Eni, Imperial Oil, ExxonMobil, Repsol, Royal Dutch Shell, Suncor Energy and TotalEnergies. Integrated O&G companies played a central role in advancing energy availability and economic growth during the twentieth century. However, these companies now face the challenge of addressing the need for a new energy transition in the twenty-first century. This transition will involve a shift from fossil fuels to alternative fuels, such as renewable wind and solar energy, battery storage and nuclear energy.
The extent to which these efforts will impact integrated oil companies (IOCs) and their assets depends on the global community's commitment to reducing demand for hydrocarbons. In the extreme, these companies may go out of business. Efforts by IOCs to adapt their business models will also affect their viability. Thus, IOCs face an important choice that will define their role in the energy transition. On the one hand, they can observe the energy transition without participating. Potential repercussions of this choice include lost business and investments and increased financial penalties in the form of carbon pricing. On the other hand, they can play an active role in the energy transition by committing to decarbonize the industry. For example, they can invest in carbon capture, utilization and storage (CCUS) and explore geothermal energy. They may incorporate solar and wind power and move toward a low-carbon hydrogen economy.
In summary, this study investigates the role of IOCs in the next major energy transition. The key points are as follows:
IOCs were leaders of the 20th century energy transition to oil. However, they are now the incumbent players in the industry and face a prisoner's dilemma in responding to the transition to green energy. IOCs have been replaced by national oil companies (NOCs), independent operators and oil field service companies (OFSCs) as major suppliers and drivers of innovation. The integrated business models of IOCs can adapt to new business practices. However, IOCs tend to lack flexibility in implementing new business practices compared to independent companies. Innovation for the energy transition requires new entrants and specialized operators, such as OFSCs, to take the lead. To respond to uncertainties around climate regulation and shifting investor sentiment, IOCs can seek partners in climate coalitions, such as the Oil and Gas Climate Initiative (OGCI), targeting cooperation. Strategic advantages in reservoir management can be used to advocate for CCUS as a decarbonization service for IOCs’ downstream consumers.
Building Up Steam, A Progressive Approach to Drilling Volcanic Geothermal Formations
Authors: Roberts Sion Llywelyn , Bailey Michael James , Victor Pawl , Sutan Mada , Prasetia Bagus , Lock Ellie 
Published: September 2022

Abstract
There are several challenges for polycrystalline diamond compact (PDC) drill bits when drilling through volcanic and interbedded applications. Roller cone (RC) bits have historically been used in geothermal applications. However, low rate of penetration (ROP), bearing life, and repairability limitations have halted progress in performance and economic gains. This paper presents game-changing PDC technology that addresses the limitations of previous conventional drill bits in a challenging geothermal application.
A reimagining of drill bit body geometries, the latest in shaped cutter technology, and durable backup elements were lab tested and customized on an unconventional drill bit chassis to maximize ROP, improve durability, and reduce downhole torque variation. The initial design phase focused on identifying and overcoming these key challenges. The second phase was to field test the new drill bit in the target application and compare it to offset runs, including roller cone, hybrid, and conventional fixed cutter bits. Key performance indices such as ROP, durability, steerability torque generation/variation, and cost per meter (CPM) were considered when evaluating the new design's performance.
Initial testing in the 16-in. section showed promising results in the field. Higher-than-average ROP and excellent interval resulted in the lowest cost per meter run. In addition, the drill bit complemented the bottomhole assembly (BHA) design well, as minimal effort was needed to keep the trajectory as planned. The delta torque generation was lower than conventional PDC bits whilst displaying higher ROP than roller cone alternatives.
The improved durability of the new design also allowed it to be run multiple times without repair, which was not possible with previous bits due to bearing hours or durability issues. This was always a challenge through the volcanic formations seen in this application. In remote locations that do not have facilities to repair drill bits, the ability to run multiple times without the need to repair is critical. The operator saved costs by not needing to transport the bit and repair any PDC cutters or secondary components after multiple runs. This outstanding run validated the benefits of the new design in terms of both technical and economic perspectives.
Does the Chow Pressure Group Analysis Provide Any New Information in the RTA of Unconventional Reservoirs?
Authors: Shekhar Jha Himanshu , Lee W. John 
Published: September 2022

Abstract
This study presents a thorough mathematical analysis of Chow Pressure Group (CPG) for unconventional reservoirs exhibiting characteristic power-law behavior and demonstrates that the CPG analysis yields the same results that traditional rate transient analysis (RTA) provides using the log-log plot between rate-normalized pressure (RNP) vs. material balance time (MBT) and Cartesian plot between RNP and timen, where n is the flow exponent. CPG analysis was proposed for flow regime identification, power-law decline-curve analysis, predicting long-term well performance from choked-back wells, and evaluating long term performance changes associated with offset frac hits. Our work shows that the presence of fracture skin may impair the CPG analysis results, while in absence of fracture skin, CPG analysis leads to the computation of the same model parameters as a standard RTA.
Our study examined the expression used to calculate CPG and shows that its formulation is closely related to β −derivative (d log(RNP)/ d log(time)). We show that the power-law model does not take fracture damage into account, and this could disguise the actual start of a flow regime resulting in a poor estimation of b−value and other model parameters using CPG. We demonstrate that the Bourdet derivative is not affected by fracture damage and leads to a more definitive flow regime identification. We further explain the CPG analysis model parameters in terms of the Wattenbarger type curve parameters for a simpler and more meaningful interpretation of the reservoir and fracture properties. We validate our hypothesis using field production data from an unconventional reservoir.
Our work presents a thorough mathematical analysis of the CPG and shows that it computes the same model parameters as standard RTA in absence of fracture damage. In presence of fracture damage, CPG could show a significant delay in identifying a unique flow regime and may result in poor estimation of b-value and other model parameters. We found that the Bourdet derivative is less sensitive to fracture damage and should be used for a more definitive flow regime identification. We recommend using CPG analysis as a complementary tool to traditional methods such as Arps decline-curve analysis for RTA of production data.
How Motor Bend Affects Stick-Slip Vibrations: Modeling and Case Study
Authors: Watt David , Dao Ngoc-Ha , Mahjoub Mohamed , Menand Stephane , Smith Mark 
Published: September 2022

Abstract
Contact friction along drill string is one of root causes of stick-slip vibrations, especially when drilling an unconventional well with a long lateral section. By using a downhole steerable motor in rotating mode, the motor bend can create important contact forces between the drill string and wellbore and consequently generate considerable frictional torques opposed to the drill string rotation. Therefore, for more accurate estimations of stick-slip behavior, these forces need to be considered in stick-slip modeling.
In this paper, a torsional vibration model based on a mass-spring system with multiple degrees-of-freedom is used to investigate the drill string stick-slip behavior. This stick-slip model includes the drill bit friction torque, contact friction along the drill string, and mud damping. The contact forces along drill string are determined using an advanced 3D drill string behavior model allowing to consider the motor bend effect. They are then integrated in the torsional equation of motion of the stick-slip model. A case study is carried out to show the consistency between the simulated results and field data.
Stick-slip is a highly damaging dysfunction that adversely effects all components of the drill string. In the case of this paper, the subject well experienced high stick-slip resulting in inefficient drilling and increased risk of equipment failure. Modelling of the drill string successfully replicated the stick-slip response observed at the rig site. Further modelling with consideration of the motor bend angle's effect successfully showed a reduction in stick-slip magnitude.
Considering the motor bend in the stick-slip model is the novelty of this work. This allows to determine more accurately the contact forces on the bottom-hole assembly (BHA), which directly influence the drill string stick-slip behavior. This work can provide a tool for the design of the BHA with a downhole steerable motor to mitigate stick-slip vibrations and lead to significant improvements of the bit performance and rate of penetration.
Flaring Management Guidance for the Oil and Gas Industry
Authors: Schopp Ulrike , Herbertson Jim , Perez Bajo Lorena , Aminu Moruf , Martin Darryl , Johnstone Harvey , Wirdak Carl , Agrawal Harshit , Gottfried Bryan 
Published: September 2022

Abstract
This paper summarizes the Flaring Management Guidance document (2021) developed in partnership by Ipieca, IOGP, and GGFR, which outlines potential options in flaring management and reduction, and offers suggestions for operational improvements and how governments and regulatory bodies can encourage and incentivize productive use of associated gas[1]. The guidance builds on the importance of knowledge sharing and collaboration as a key step in overcoming the barriers to both the reduction of flaring and the use or conservation of associated gas. Its novelty originates from a shared understanding developed over many years by the oil and gas industry, regulators, and other stakeholders that support sustainable growth and align their commitments to achieve the UN Sustainable Development Goals[2].
Analysis of Dynamic Reservoir Behaviour Reflected in Long-Term Surveillance Data. A Workflow Driven by Pressure Rate Deconvolution Combined with Reservoir Simulation.
Authors: Levitan Michael , Thatcher Alexander , Roberts William , Johnson Piers 
Published: September 2022

Abstract
Reservoir appraisal programs prior to field development sanction typically do not fully assess the entire reservoir in terms of properties, connectivity, and architecture. The well tests performed on appraisal wells are relatively short and the dynamic data acquired during these tests reflect reservoir properties in the near well region. The properties on the scale of the entire reservoir are reflected in the dynamic pressure and rate data acquired on a much longer time scale and for practical and environmental reasons such data can only be obtained during production operations after the field is in production. It has become a routine practice in new field developments to equip development wells with permanent downhole pressure gauges. Analysis of this pressure data with the well rate early in the field life creates an opportunity to adjust the development plan based on the understanding of reservoir properties, its connectivity, and architecture. This paper presents a methodology for reservoir dynamic analysis that incorporates the information from surveillance pressure and rate data and integrates this analysis with geological and seismic data interpretation reflected in reservoir maps. This is a generalization of pressure transient analysis technology to the analysis of dynamic data on a much longer time scale and to the multi-well environment. This is not a straightforward generalization because these longer temporal and spatial scales bring into focus additional physical factors that must be accounted for in the analysis.
This paper demonstrates the application of this analysis approach and of the entire workflow to real surveillance data from a gas reservoir. The technique presented does not impose any special requirements on data quality. The analysis approach and the entire workflow yield a much clearer picture of the reservoir and allow engineers to develop profound insights and draw important conclusions which guide and influence decision-making processes. The limitations of this workflow are also discussed, and advice is provided as to when the approach presented can be used and when the underlying principles associated with PTA break down.
Removal of Wax Deposit from Tight Formation Using Ultrasonic Cavitation with Thermochemical Heat Source
Authors: Alade Olalekan , Abdullah Eassa , Alfaraj Mashaer , Al Hamad Jafar , Hassan Amjed , Mahmoud Mohamed , Al Shehri Dhafer , Ivan Solling Theis , Nakhli Ayman 
Published: September 2022

Abstract
Formation damage phenomenon constitutes serious operational and economic problems to the petroleum production. Oil production in certain reservoirs is inadvertently impaired by precipitation and deposition of the high molecular weight components such as paraffin wax. A facile applicability of synergistic effects of thermochemical reaction and ultrasonication to mitigate wax deposition has been presented in this article. Thermochemical heat source has to do with exothermic heat generation from certain chemical reactions. On the other hand, ultrasonication causes cavitation and implosion of bubbles, which is trasimmted as energy in the medium and assit in detaching contaminants from the surface. Series of imbibition experiments were conducted at different ultrasound frequencies (low 28kHz, and high 40kHz), exposure times (20, 40, and 60 mins), and different molarities (M1, M2, and M3) of the thermochemical fluids (TCF), for removing wax deposit from tight Scioto Sandstone core samples. The performance was followed through permeability and porosity tests, as well as Scanning Electron Microscopy with Energy-Dispersive X-ray (SEM-EDX) analyses. Ultimately, the results revealed promising potentials for the proposed technology for efficient paraffin wax removal from a tight rock sample up to 70% within the experimental limits investigated.
Bayesian Networks Applied to ESP Performance Monitoring and Forecasting
Authors: Dussi Simone , Octaviano Ryvo , Shoeibi Omrani Pejman 
Published: September 2022

Abstract
Predicting the degradation of the Electrical Submersible Pumps (ESP) performance well in advance to avoid its failure is a rewarding yet challenging task. The complex interdependencies with well-reservoir properties and between machine components influence ESP performance and ultimately can lead to different types of failure. Predictions based on typical data-driven approaches are hampered by data quality and lack of variations in the operating conditions. In this study, Bayesian Networks (BNs), where expert knowledge can be incorporated and prediction uncertainty can be easily assessed, were tested as a potential method for performance monitoring and forecasting of a single ESP based on condition monitoring and production data.
Different BNs based on auto-learned structures (relations being learned by the machine learning model) and expert specified structures were evaluated in the project. Network variables were selected from a dataset containing time-series sensors data (Pressure, Temperature, Frequency, Voltage, Current, etc.) for ESPs which degraded due to different reasons, including electrical failures and well conditions. The pump hydraulic efficiency, an indicator for the machine health, was used as a target variable.
We report the results for a selected case where the ESP failed due to electrical failure (downhole ground fault). The hydraulic efficiency showed a noisy unsteady decreasing trend for several months before the failure. A sliding-window forecasting of the pump efficiency was performed with time horizons varying from few hours to several weeks. Based on only 4 sensors (current, two pressures, vibrations), the results between BNs with different structures were compared. The effects of adding additional variables (such as motor temperature or flowline pressure) to the network were also studied. A small user-defined BN was able to predict the pump hydraulic efficiency with an average absolute error ranging from 1.1% for the next 48h to 2.5% for the next 10 days.
The novelty of this study is the application of BNs to ESP performance monitoring and forecasting. Within this framework, expert knowledge can be included by explicitly defining the causality between variables, suggesting a way to enhance data-driven methods. Since the prediction is probabilistic, the confidence in the predicted value can be straightforwardly assessed.
A Machine Learning Approach to Rock Typing with Relative Permeability Curves Using Kmeans Clustering Algorithm
Authors: Muoghalu Amalachukwu Immaculata 
Published: September 2022

Abstract
Relative permeability is an important petrophysical property that characterizes the dynamic movement of one fluid to another during the production of oil and/or gas in the subsurface. Numerous methods for rock typing exist in the industry, but classification based on the effect of relative permeability is often deficient. These rock typing methods which are manually modeled or simulated can still be erroneous as the rocks are characterized based on human observation. Therefore, there is a need for a simple yet accurate rock typing method that automatically classifies the rocks based on the observations of the data without bias. This paper proposes a machine learning clustering method using relative permeability as a proxy for other rock properties such as porosity and lithology. The proposed method applies the K-means algorithm to obtain clusters of different rock types based on the shape and characteristics of relative permeability curves.
In this study, the machine learning algorithm was implemented using the Python programming language and tested on real-life data sets obtained from oil-field core samples. The result obtained was a model which clustered the samples into four different groups of rock types which were qualified as ‘best, better, good, and poor’ from the observation of the reservoir properties of the different rock types clusters and also by comparing the recovery factors obtained from waterflooding performed on the core samples of the rock. The results obtained from using permeability versus porosity (K-phi) plot and the relative permeability as features showed a discrepancy with that obtained using relative permeability alone, as it produced a better clustering of the rock types incorporating flow dynamic into rock typing.
The study give a simple, accurate, and fast method of rock typing based solely on the observation of the data with little or no bias and illustrate the importance of relative permeability inclusion in rock typing.
Evaluation of the Oil Recovery and Economic Benefit of the First-Ever Polymer Flood Field Pilot to Enhance the Recovery of Heavy Oils on Alaska's North Slope using Machine-Assisted Reservoir Simulation
Authors: Keith Cody Douglas 
Published: September 2022

Abstract
The first-ever polymer flood field pilot to enhance the recovery of heavy oils on the Alaska North Slope is ongoing. This study seeks to evaluate the oil recovery and economic performance of the project via machine-assisted reservoir simulation. First, a reservoir simulation model is calibrated to the production data through the introduction and modification of transmissibility contrasts. Machine-assisted history matching techniques are crucial to the success of this procedure. To replicate the early water breakthrough observed during waterflooding, transmissibility contrasts are emplaced in the reservoir model to force the viscous fingering behavior expected when water is used to displace this 330 cp heavy oil. After injection is switched to tertiary polymer flooding, the transmissibility contrasts are reduced to replicate the significant decrease in the producing water cut. This behavior indicates the dampening of viscous fingering effects, which is expected from the switch to a less mobile injected fluid. Later, transmissibility contrasts are reinstated in the simulation model to recreate a producing water cut surge. This surge indicates a decrease in the injection conformance, likely from the overextension of fractures developed at the injecting wells. Next, oil recovery forecasts are produced using calibrated simulation models from each stage of the history matching process. These production forecasts are then input into an economic model, incremental to waterflooding expectations. The decision to pursue incremental economic analysis is fit-for-purpose, allowing for a focused evaluation of the decision to switch from waterflooding to polymer flooding whilst canceling out a number of impertinent and uncertain cash flows. In all cases, the forecasts demonstrate that the polymer flood will produce a much greater heavy oil recovery than waterflooding, yielding attractive project economics even under conservative oil price and polymer cost assumptions. Thus, we conclude this polymer flood field pilot is both technically and economically successful. However, significant variations in recovery and economics between the simulation scenarios indicate that a simulation model only remains valid for prediction if the flow structure in the reservoir remains consistent with its historic behavior. Thus, a simulation model calibrated for waterflooding may not capture the full technical and economic benefit of polymer flooding or other enhanced oil recovery processes. Furthermore, the overextension of fractures from injecting wells reduces the expected performance of the polymer flood, perhaps necessitating future conformance treatments.