bluuebunny/crossref_metadata_2025_split huggingface dataset:
Finding Optimal Motor Valve Open Triggers in Plunger Lifted Wells with Offline Reinforcement Learning
Authors: Mayfield Walter , Lopez Felipe , Yu Yingwei , Wang Haozhu 
Published: October 2023

Abstract
Reinforcement learning is a novel approach for artificial lift in which optimal control policies are learned through interactions with the environment. This paper reports the first implementation of reinforcement learning for plunger-lifted wells using only historical data in an offline approach. The use of offline reinforcement learning allows production engineers to remain in control during the exploration phase, instead of allowing the agent to explore set-points that could result in unsafe operation. This paper explores the application of the Conservative Q-Learning (CQL) algorithm to maximize production in a small-scale plunger-lifted well model that mimics a low-pressure gas well controlled by the "minimum on-time" framework. The well was designed to receive a new open-trigger value for the motor valve from the CQL agent at the beginning of every cycle. The reinforcement learning problem was formulated to maximize gas production (reward) by adjusting the "casing-line" open trigger value (action) given the volume of fluid in the volume after the well has settled (state). Experimental results demonstrate that a dynamic policy that modulates the open trigger set point based on slug volume improves the average production rate by 35% compared with a static casing-line pressure trigger. The optimal policy suggests that using higher casing pressure open triggers for large slug volume cycles improves long-term production in plunger lift.
Practices of Low Carbon Transition of Tarim Oilfield
Authors: Deng Qianxing , Shen Fuxiao , Wang Kelin , Gao Jieyu , Li Ruyong , Wen Zhang , Wei Zhiqiang , Guo Xinyong , Liu Guode , Xia Xianwei , Miao Xinyue 
Published: October 2023

Abstract
The goals of carbon emission peak in 2030 and carbon neutrality in 2060 were put forward by China in 2020. However, the production of Tarim Oilfield reached 33.1 million toe(tonnes of oil equivalent) in 2022, with total energy consumption of 2.46 million tce(tonnes of coal equivalent) and total carbon emission of 2.2985 million tCO2e(tonnes of carbon dioxide equivalent). And the production is expected to exceed 40 million toe by 2025. Energy consumption and carbon emission remain increasing if effective measures are not taken, therefore, and low carbon transition of Tarim Oilfield is urgent.
In this context, greater goals that carbon emission peak by 2025 and carbon neutrality by 2040 have been proposed, and some measures have been taken. According to energy saving and consumption reducing, technical means like process optimization and torch extinguishment are adopted to strictly control energy consumption of new projects. As for substitution of clean energy, distributed photovoltaic, waste heat utilization and other measures are used to decrease carbon emission of the established production system. Meanwhile, CCS/CCUS pilot projects are carried out. Energy conservation, carbon emission reduction and substitution of clean energy are promoted by demonstration area. According to clean energy supply, large amounts of land such as deserts and Gobi in western China can be utilized, and many factors like natural resources, consumption market and power grid structure are also considered to develop new energy power generations which may be photovoltaic power station, solar thermal power plant.
Results of the net zero transition of Tarim Oilfield were remarkable last year. As for energy conservation and carbon emission reduction, two demonstration areas, the first zero carbon desert highway in China and Lunnan Oilfield were built with carbon emission of -14,000 tCO2e and 6,600 tCO2e respectively. And total energy consumption decreased by 0.46%, energy use decreased by 8.41% in intensity, and carbon emission decreased by 0.57% in intensity. As for clean energy supply, two 100 megawatts(MW) photovoltaic power stations have been built, generating electricity of 400 million kWh and reducing 311,700 tCO2e per year. And photovoltaic power stations with total capacity of 1100 MW are under construction, which are expected to generate 1.919 billion kWh and reduce 1.4955 million tCO2e annually. And the clean energy will not only meet its own demand, but also be provided for other enterprises.
Application of Digital Twin for Real-Time Early Identification and Warning of Pipe Stuck Using Hybrid Approach of Physics Model and Machine Learning
Authors: Zhang Jiawei , Huang Hongchun , Wang Haige , Wang Qing , Ji Guodong , Li Hong 
Published: October 2023

Abstract
Pipe Stuck is one of the predominant causes for drilling-related non-productive time (NPT). According to statistics, losses up to hundreds of millions of dollars are caused by pipe stuck globally each year. Early identification and warning of pipe stuck are essential for predicting and avoiding stuck issue once preventive measures are taken promptly. Typical detecting methods based on data fluctuation amplitude or curve trends subject to expert user experience and human error. Moreover, pipe stuck usually have already happened in most cases when noticed by these methods.
This paper presents an innovative system combing physics model with machine learning method, which enables automatically pipe stuck risk monitoring and warning while drilling. The new system combines these approaches: 1)real-time calculation of hook load, torque and standpipe pressure(SPP) for 9 different drilling states (rotary drilling, slide drilling, reaming, back-reaming, tripping in, tripping out and etc.) based on drill string mechanics and wellbore hydraulics model; 2)The calculated parameters are checked and calibrated automatically within set computing cycle to narrow down calculation error; 3) The deviation of actual parameters from theoretical modeled value and rate of change(ROC) of actual parameters are calculated and compared on time-series, based on which a pipe stuck risk index (PSRI) is proposed ranging from 0% to 100%; 4) With different risk index ranges, pipe stuck risk level is monitored and evaluated as low (green), medium(yellow) and high(red) on real-time basis. For historical wells with and without pipe stuck issues under different drilling states, thresholds for deviation and ROC of parameters corresponding to different risk levels are obtained by random forest algorithm (RFA).
The system is designed to provide users, driller or monitoring specialist a warning signal with prompt window jumping out and constant beep sound whenever a high-risk is identified. Once detected, preventive or intervention measures are generally mandatory for on-site personnels. Application cases showed that alerting warning of high-risk of pipe stuck could be realized typically between 30 minutes and 2 hours ahead during back-reaming and tripping operations, which demonstrated the system capability for early identification and warning of pipe stuck under different drilling states.
The new system showed that digital twin driven technique could provide effective solution for detecting the onset of pipe stuck, which could help to minimize NPT, better capture and utilize digital data for optimizing drilling.
Enhancing Drilling Operations and Management Through Digital Ecosystem
Authors: Zhang Jing , He Xusheng , Dai Yong , Wang Jinlu , Zhu Jun 
Published: October 2023

Abstract
The digitalization of the drilling process has revolutionized the oil and gas industry by incorporating digital technologies and data analytics into drilling operations. This transformative shift aims to optimize operational efficiency, enhance safety measures, and enable prompt decision-making.
In this paper, we will look into the various benefits of creating a digital ecosystem for drilling, focusing on improved operational efficiency, potential risk identification, advancements and application in smart equipment, enhanced communication and collaboration, and the reduction of operational expenses. We will also discuss a practical case study that demonstrates the effectiveness of digital ecosystem in reducing costs, minimizing downtime, and enhancing overall drilling performance.
Furthermore, the integration of VR simulation with digital twin models in the digital ecosystem for drilling holds immense potential for operators. By providing a realistic and immersive training experience, operators can optimize their drilling operations, minimize risks, and ultimately save costs. Through this exploration, we aim to highlight the transformative power of digitalization in the drilling industry and showcase the benefits that can be achieved by embracing this technology-driven approach.
Experimental Study on the Compatibility of Hydrate Inhibitor and Anti-Scale Agent
Authors: Xiuan Sui , Zhiyuan Wang , Shikun Tong , Yukun Guo , Jianbo Zhang 
Published: October 2023

Abstract
In the process of deepwater oil and gas development, hydrate formation and scale formation are easy to occur, and in serious cases, flow barriers will occur and threaten platform safety. A large amount of ethylene glycol and scale inhibitor is usually injected to ensure the safety of wellbore flow. However, due to the limited number of tree chemical pipelines, the joint injection of ethylene glycol and scale inhibitor is usually adopted. The inhibition efficiency of ethylene glycol and anti-scale agent on hydrate and scale was studied by single and compound evaluation experiments. In this paper, four kinds of high temperature anti-scale agents were selected based on Environmental conditions of the selected gas well, and compatibility evaluation experiments were carried out in combination with hydrate inhibitors to study the interaction between anti-scale agents and hydrate inhibitors. The experimental results show that PESA has high anti-scaling efficiency with MA/AA under the selected gas well conditions. But the hydrate conversion of MA/AA in the presence of ethylene glycol is low, and MA/AA has better scale prevention efficiency than PESA. Therefore, MA/AA has good compounding property with ethylene glycol and is suitable for the co-injection scheme of anti-scale agents and ethylene glycol.
Leveraging First-Ever OBN in India for Reservoir Characterization and Near-Field Exploration in a Carbonate Field at Offshore Mumbai, India
Authors: Soni Rajesh , Goswami Saumya Kanti , Mukherjee Anubrati , Elsayed Raghad , Mishra Uday , Basu Subhankar , Mahapatra Srilata , Prakash Anand , Singh Anoop 
Published: October 2023

Abstract
This study had been performed to map the lateral and vertical heterogeneity of carbonate reservoir, using the newly acquired Ocean Bottom Node (OBN) data. Joint PP-PS pre-stack inversion has been performed to delineate the facies distribution and associated properties such as porosity and clay volume. This study greatly enhanced the depositional understanding of the area and helped to identify the in-fill well drilling and near-field exploration opportunities and provided attributes to update the existing reservoir model.
Practical Application of 3D Dubins Curve Method in Directional Well Trajectory Planning
Authors: Liu Haoge , Gjersvik Tor Berge , Faanes Audun 
Published: October 2023

AbstractThis paper introduces the practical application of 3D Dubins curve in directional well trajectory planning to handle more complex downhole conditions such as consecutive targets and various constraints in the underground formation. To handle consecutive targets in a direction well, we can simply treat the whole trajectory as a combination of multiple 3D Dubins curves with the undetermined drilling directions at the targets as additional optimization variables. As for the formation constraints, practical drilling may require the well trajectory to avoid certain areas or/and control the wellbore inclination in some layers to maintain wellbore stability. To handle such a problem, we can adjust the depth of kickoff point(KOP) or use a more complex trajectory structure with more curved sections rather than a simple trajectory structure with at most two curved sections (build-up & drop or two build-ups). The optimal trajectory generated by our method is the shortest smooth path satisfying all given constraints, besides, it has a fixed curvature in the curved section so that the tool face angle does not need to change so frequently during drilling. Our method was adopted by the NTNU Drillbotics team to design the optimal trajectory for consecutive targets, contributing to the NTNU championship in the 2022 SPE Drillbotics Competition. The advantages of our method in the complex application scenarios mentioned above are demonstrated in the case studies with comparison to a previously published result and real field data provided by Equinor.
A Deep Learning Framework to Forecast Spatial-Temporal Dynamics of CO2 Mineralization in Reactive Rocks
Authors: Tariq Zeeshan , Yan Bicheng , Sun Shuyu 
Published: October 2023

Abstract
Reactive rocks, such as basalt, are composed of a variety of minerals, with pyroxene, olivine, and plagioclase feldspar being among the predominant minerals. When exposed to carbon dioxide (CO2)-charged waters, these rocks can undergo a series of reactions, leading to the formation of stable carbonates. These carbonates can store carbon for thousands of years, contributing to climate change mitigation. To better understand the interplay between CO2 and brine in these reactive formations, numerical simulations are a very useful tool. However, simulating fluid flow in these reservoirs can pose significant computational challenges. The inherent reactivity of various minerals complicates the modeling process, leading to computationally expensive simulations. Therefore, the objective of this study is to develop a deep-learning workflow that can predict the changes in CO2 mineralization over time and space in saline aquifers, offering a more efficient approach compared to traditional physics-based simulations. To achieve this, a numerical simulation model was created to replicate the CO2 injection process in saline aquifers. The model was then sampled using the Latin-Hypercube method, considering various parameters related to petrophysics, geology, reservoir, and decision-making. These samples generated a comprehensive training dataset of approximately 700 simulation cases, forming the basis for training the UNet model, a type of convolutional neural network. The UNet models were trained, incorporating information on reservoir properties, well characteristics, and time, enabling the prediction of mineral precipitation at different spatial and temporal scales. During the training process, the root-mean-squared error (RMSE) was used as the loss function to prevent overfitting. Evaluation of the trained UNet model was performed using three error metrics: the normalized root mean square (NRMSE), mean absolute percentage error (MAPE), and the coefficient of determination (R2). The model achieved an R2 value of 0.998 for training and 0.991 for testing, indicating its accuracy in predicting the evolution of mineral concentrations over time and space. The MAPE for all mappings was approximately 5%, demonstrating the effectiveness of the trained model. In terms of computational efficiency, the UNet model’s prediction CPU time per case was remarkably fast, averaging only 0.2 seconds. This is significantly faster compared to the time required by the physics-based reservoir simulator, which took 21600 seconds per case. Thus, the proposed method not only provides accurate predictions comparable to physics-based models but also offers substantial computational time savings. The deep learning models developed in this study offer a computationally faster alternative to traditional numerical simulators for assessing mineralization trapping in geological carbon storage (GCS) projects, specifically concerning the mineral trapping mechanism.
AI-Enabled Offshore Circular Economy: Tracking, Tracing and Optimizing Asset Decommissioning
Authors: Suboyin Abhijith , Eldred Morgan , Sonne-Schmidt Carsten , Thatcher Jimmy , Thomsen Jens , Andersen Ole , Udsen Ole 
Published: October 2023

Abstract
The objective of the industrial case study is to address the challenge of efficiently managing end-of-life offshore assets while meeting established ESG targets. The offshore industry uses substantial amounts of steel, and current practices for handling these assets are often not transparent, leading to criticism for unsustainable and non-compliant scrapping procedures. The presented approach combines engineering and technical data from offshore assets with decommissioning best practices and digital and Al technologies to ensure a transparent and ESG-compliant approach to scrapping. This will provide auditable evidence of ESG compliance and is expected to address the challenge of managing end-of-life offshore assetswhile providing sustainable metals alternatives for heavy industry.
The use of Life Cycle Assessment (LCA) requires asset owners to demonstrate compliance with net zero targets, which is mainly impacted by the main material groups, such as steel. Recycled steel maintains its strength properties and if produced using electric arc furnaces and transported efficiently emissions can be minimized. The implemented solution offers a transparent, reliable, and ESG-optimized approach for scrapping assets. Each step in the process, from cutting to transportation to the metals furnace, can be tracked and audited, ensuring the end-product can be classified as ‘green’ steel. This steel can be used by asset owners to construct new vessels or facilities or can be sold to other users, creating a true circular economy where a significant amount of embodied carbon can be recycled and made net zero.
The use of advanced digital technologies in the offshore industry's scrapping and recycling process allows for a more transparent and sustainable approach. The procedure includes verification of environmental, social, and governance (ESG) parameters and information on carbon emissions, enabling the selection of the most environmentally friendly approach. This can provide transparency and stability in sourcing, ensure ESG compliance, and result in competitive pricing. Integrating the use of recycled metals, which has significantly lower emissions compared to non-recycled metals, can further decrease resource consumption and emissions while increasing the value of recycled by-products. Under this industrial study, up to 4.3 tons of CO2 can be saved for each ton of recycled stainless-steel scrap utilized. Furthermore, the breakdown of emissions in terms of handling, transportation and operations are illustrated in depth with validated emission factors.
The solution can be expanded to cover a variety of materials and assets, both offshore and onshore, to give asset owners peace of mind regarding their ESG responsibilities in the scrapping and construction value chain. This will provide a smooth, transparent, and efficient turnkey solution.
Corrosion Study of a Trunk Line of Western Offshore of India
Authors: Talukdar Maushumi Kakoti , Bareja Simran , Saini Arzoo 
Published: October 2023

ABSTRACT
The Oil & Gas produced in one of the western offshore fields are carried through long distance pipelines to the onshore units, where they are processed for marketing. A number of leakages were reported in the Oil pipeline at different locations. It was commissioned in 2005 and the first leakage was observed in 2016 near the platform from where it is originating, and there after subsequent leakages were observed in 2018, 2022 and 2023. On analyzing the internal environment of the pipeline, the probable contributing factors were deduced. The water cut in the pipeline was reported as 1.5%, which shows the internal system remains oil wetted. The software study revealed the flow regime as transient. The microbial data shows the presence of SRB in the range of 102 per ml. As given in the leakage history, the leak location is primarily the butt weld in majority incidences with initial 26 kms of the pipeline. The bathymetry shows that there is sag and undulations in the segment of frequent leakage zone. As per thickness reduction data, there is severe metal loss in the nearby region of the leakages. The temperature profile shows that the temperature is in the range of 40-50°C in the leaked portions of the pipeline. This temperature range works best for bacteria to flourish and enhance their activity substantially4. It is also observed that, the H2S concentration is approximately 1500 ppm at the onshore end of the pipeline. However, the concentration of H2S at the platform ends of lines that are connected to this main pipeline was reported to be within 400 ppm. In another study carried out at our institute in 2009 it was observed that at that time period the H2S concentration of this pipeline was around 300 ppm at the onshore end. These facts also support the proliferation of SRBs at the sag bends of the pipeline, whose presence has resulted in substantial increase in H2S in the pipeline. However, in the segment where there are no significant undulations, no leakage is reported till date. MTM survey also shows the thickness reduction in this segment is mostly in less severe zone. As the pipeline samples were not available for analysis but with all the supporting data it may be concluded that the leakages were mainly due to microbial induced corrosion (MIC). Adequate recommendations are deliberated in the technical paper. In the Gas line, the CO2 is present in range of 2.65 - 2.81 mole % while H2S concentration remains in range of 97 - 105 ppm. It is CO2 dominated system. The SRB count in the line is 105 which is very high. The high content of Iron in the Gas line shows that the corrosion is taking place and the line is susceptible to leakages. This study will help in deciding the future strategy of the lines.
Unveiling the Inhibitory Potential of Gingerol Substructures against SARS- CoV-2 RBD: An Integrated Ensemble Learning and In Silico Screening Approach
Authors: R. Sherin D. , R. Linda S. , A. Akhila 
Published: October 2023

Abstract
In the relentless quest for effective treatments against SARS-CoV-2, extensive exploration of potential inhibitors has been underway. In this study, we present an integrated approach combining machine learning and in silico screening to identify promising inhibitors for the SARS-CoV-2 receptor-binding domain (RBD). We harnessed a dataset of Vina scores for 988 gingerol substructures, employing Random Forest (RF) regression as the optimal model to predict Vina scores accurately (R² = 0.77). Virtual screening, both through RF predictions and PyRx, consistently highlighted 14 molecules with inhibitory potential. Pharmacokinetic evaluation, aided by the Bioavailability Radar and a BOILED-Egg simulation, further refined the selection of four leads-G4, G5, G11 and G13 with human intestinal absorption, out of which the P-gp non substrate G13 (PubChem CID: 135196841) can be act as a promising candidate. Molecular docking, molecular dynamics simulations, and Density Functional Theory (DFT) calculations validated the stability and interactions of this compound with the SARS-CoV-2 RBD. Our study offers a streamlined methodology for identifying potential inhibitor, paving the way for further experimental validation.