bluuebunny/crossref_metadata_2025_split huggingface dataset:
Boosting Algorithm Choice in Predictive Machine Learning Models for Fracturing Applications
Authors: Khan Abdul Muqtadir , BinZiad Abdullah , Subaii Abdullah Al 
Published: October 2021

Abstract
With the advancement in machine learning (ML) applications, some recent research has been conducted to optimize fracturing treatments. There are a variety of models available using various objective functions for optimization and different mathematical techniques. There is a need to extend the ML techniques to optimize the choice of algorithm.
For fracturing treatment design, the literature for comparative algorithm performance is sparse. The research predominantly shows that compared to the most commonly used regressors and classifiers, some sort of boosting technique consistently outperforms on model testing and prediction accuracy. A database was constructed for a heterogeneous reservoir. Four widely used boosting algorithms were used on the database to predict the design only from the output of a short injection/falloff test.
Feature importance analysis was done on eight output parameters from the falloff analysis, and six were finalized for the model construction. The outputs selected for prediction were fracturing fluid efficiency, proppant mass, maximum proppant concentration, and injection rate. Extreme gradient boost (XGBoost), categorical boost (CatBoost), adaptive boost (AdaBoost), and light gradient boosting machine (LGBM) were the algorithms finalized for the comparative study. The sensitivity was done for a different number of classes (four, five, and six) to establish a balance between accuracy and prediction granularity. The results showed that the best algorithm choice was between XGBoost and CatBoost for the predicted parameters under certain model construction conditions. The accuracy for all outputs for the holdout sets varied between 80 and 92%, showing robust significance for a wider utilization of these models.
Data science has contributed to various oil and gas industry domains and has tremendous applications in the stimulation domain. The research and review conducted in this paper add a valuable resource for the user to build digital databases and use the appropriate algorithm without much trial and error. Implementing this model reduced the complexity of the proppant fracturing treatment redesign process, enhanced operational efficiency, and reduced fracture damage by eliminating minifrac steps with crosslinked gel.
Unlocking ESP Potential in High Gas/Liquid Ratio Wells Using Gas Lock Prevention Control in West Java Field, Indonesia
Authors: Saputra Muhammad Nur Wangsa , Ciptaningsih Rahmi , Febryana Niken Endah 
Published: October 2021

Abstract
West Java field operated 60 electric submersible pumps (ESPs) as a lifting method with 2.850 BOPD production contribution. These ESP wells produce from a mature structure. At one point, 48% of ESP operation were shut down due to ESP non-optimum operation. The challenges in ESP operations in the asset are high gas-liquid ratio (GLR), impurities, sand and scale buildup, and well integrity where high GLR was deemed as the major problem that deteriorated the ESP's performance.
Conventional ESPs gas separator were installed in the field, but the gas-handling device could not handle more than 45% free gas while some wells have more than 50% free gas. Three wells in particular were assessed, Well A, Well B, and Well C, which have 585, 1196, and 1690 average GLR respectively. These wells had problem with reduced pump efficiency and very low run life due to frequent ESP trips which were caused by the gas lock problem. A solution to maintaining oil production was by changing the production zone to zone that producing less gas and by installing more advanced gas-handling device. However, the probability of experiencing high gas production from new zones can't be ruled out therefore other mitigation plan had to be found.
Gas lock protection control is an algorithm that manipulates ESP real-time rotational speed to prevent gas interference inside pump. The algorithm was introduced as a mitigation measure and commissioned in July 2020 at Well A where it directly optimized production by 16%. To prove the robustness of the gas lock prevention control, the project was then extended to Well B and Well C which began to implement gas lock prevention control in August 2020 to handle the increase of their gas production. Thanks to this gas lock prevention control, the wells have been able to maintain production without spending either time or money to change production zone or to change the ESP completion.
Going forward, gas lock protection control will be set as an option on ESP devices. Thus, unplanned gas interference effects may be reduced in other wells that being produced by ESP thereby helping to maintain production at an optimum level.
Optimal Design and Scheduling of Offshore Oil and Gas Field Development with Environmental Impact Consideration
Authors: Nugraheni Novita Dwi Putri , Li Jie 
Published: October 2021

Abstract
The objective of the paper is to develop a mixed integer nonlinear programming (MINLP) model for optimum design and scheduling of offshore oil and gas field development in respect to simultaneous consideration of economic and environmental impact. The model is utilized as a tool for decision making management in conceptual stage. Nonlinear reservoir behavior and floating demand constraint are incorporated to improve accuracy of the solution.
This paper utilizes mathematical programming techniques to address the design and scheduling problem of offshore oil and gas field development. Field development problem is first formulated into a multi-objective MINLP model incorporating many realistic features such as nonlinear reservoir behavior and floating demands. The objectives are to maximize net present value (NPV) and minimize total environmental impact (TEI) simultaneously. Environmental impact is assessed using the ReCiPe2016 method. Augmented ε-constraint method (AUGMECON) is then employed to solve the proposed multi-objective MINLP model to generate the Pareto-optimal front that is able to assist decision maker selecting the most preferred solution.
The performance of the proposed modelling framework is investigated on a set of problem which consists of 2 reservoirs, 2 FPSOs, 2 customers and 5-years planning horizon. First model with single objective function to maximize NPV can be solved effectively within short computational time. The solution gives optimum decision of design, investment, production schedule, and transportation regardless the environmental impact. Then, simultaneous optimization of multi-objective MINLP with different value of ε-constraint generates multiple development schemes and objective function values. The results indicate trade-off between maximizing NPV and minimizing TEI. It is possible to obtain maximum NPV of USD 2.4 trillion at the expense of TEI which is 307.518 or to generate minimum TEI of 16.65 at the expense of NPV which is USD 74.368 billion. All possible solutions within extreme values range are presented in form of a Pareto-optimal front where TEI and NPV are plotted in x and y-axis respectively. It will assist the company to select the most preferred solution based on NPV. Consequently, the selected option brings corresponding value of TEI. Additionally, the Pareto optimal front also allows decision maker to have more flexibility to compromise between economic and environmental issues.
This is the first study to consider environmental impact in the offshore oil and gas field development. Many realistic operational features such as nonlinear reservoir behavior and floating demands are also incorporated. In addition to that, the proposed framework yields a powerful tool to assist decision maker selecting the most preferred solution that satisfies their criteria in both economic and environmental aspects.
Risk Assessment and Enhanced Oil Recovery Effect of Gravity Assisted Oxygen-Reduced Air Flooding
Authors: Huang Lijuan , Li Zongfa , Ren Shaoran , Liu Yanming 
Published: October 2021

Abstract
The technology of air injection has been widely used in the second and tertiary recovery in oilfields. However, due to the injected air and natural gas will explode, the safety of the gas injection technology has attracted much attention. Gravity assisted oxygen-reduced air flooding is a new method that eliminates explosion risks and improves oil recovery in large-dip oil reservoirs or thick oil layers. The explosion limit data of different components of natural gas under high pressure were obtained through explosion experiments, which verified the suppression effect of oxygen-reduced air on explosions. The influence of natural gas composition and concentration on explosion limits was also investigated. In addition, a rotatable displacement device was used to study the feasibility of gravity assisted oxygen-reduced air injection for improving the heavy oil reservoirs recovery. Under pressure and temperature conditions of 20MPa and 371K, the sand-filled gravity flooding experiments with different dip angles were carried out using oxygen-reduced air with an oxygen content of 8%. The results show that with the increase of the reservoir dip, the pore volume of the injected fluid at the gas channeling point, the efficient development time of gas injection, and the final displacement efficiency of gas injection development all increase through gravity stabilization caused by gravity differentiation. In the presence of a dip angle, the cumulative oil production before the gas breakthrough point exceeded 80% of the oil production during the entire production process, indicating that gravity assisted oxygen-reduced air flooding is an effective and safe improving oil recovery method. Finally, the explosion risk of each link of the air injection process is analyzed, and the high-risk area and the low-risk area are determined.
The Challenges and Lessons Learned from the Unanticipated Long Run Well Unloading Campaign in T Field, Malaysia
Authors: W Hassan W M Faris , Thamilyanan Thivyashini , Suggust Alister , Usop M Zulfiqar , Hamdan Nurfatrina , M Razali Abdullah , Latif Nurlizawati , Bakar Hasmizah , Kumar Suman 
Published: October 2021

Abstract
This paper presents overall challenges and experiences that the team were facing during offline well unloading campaign recently. On paper, offline well unloading is more favorable as an option to minimize environmental impact, economize rig days and reuse produced clean-up oil. This practice is not new. The implementation of this practice to a 40-year-old brown fields led to successfully bring adopted for two drilling campaign. The well unloading to the existing facilities requires substantial strategies to be adapted and possibilities to handle drilling fluids and solid/debris are extensively discussed within multi disciplinary team whereby risk evaluation was conducted ensure the good progress of this operation. The assurance part to handle production of solid/debris along with produced fluid to surface within 12 hours operation with the assitace from Hook-Up and Commissioning (HUC) work barge initially and resumed later by daily trip via boat to offshore site will be discussed in this paper. Almost 90% of this activity is carried out with crews mobilized with 45-minute boat trip from Bintulu to site, highly depended on weather condition. This paper also will share the major challenges that resisted the good progress of unloading for four wells such as limitation associated setting of the surface Hi Lo Pilot safety switch that cannot handle the transient flow condition, huge pressure drop exerted by the inline surface strainer that temporarily installed to filter any solid production, unintentional jammed close of Tubing Retrieveable Surface Controlled Subsurface Safety Valve (TRSCSSV), unanticipated low performance of gas lift compressor that restricted the wells to continuously flow to the test separator and temporary high H2S gas release throughout the well unloading operation. Due to daily tripping, the crew left with constrained time of 4 - 5 effective hours only per day in attempts to kick off and flow the wells. On paper, offline well unloading indeed offered 70% cost saving compared to clean up with rig. However, in actual it resulted to increase 3 times in well cost and delay in achieving first production target which was 6 months later from the plan due to unanticipated causes that prolong the unloading activities.
Successful Water Shut Off Strategy for Multilayer Tubingless Wells at Mahakam Field: JM-X Case Study
Authors: Umar Khalid , Rahman Risal , Hidayat Reyhan , Kurniawati Pratika Siamsyah , Marindha Rantoe , Dahnil Gitani Tsalitsah , Pancawisna Gerardus Putra , Hidayat Danny , Az-Zariat Arasy , Utama Fia 
Published: October 2021

Abstract
The objective of this paper is to present the Mechanical Water Shut-Off (MWSO) strategy for multilayer reservoirs on tubingless well. With 10 open perforated reservoirs and no selectivity option, isolation on water producing reservoir will be the main challenge since production is commingled throughout the lifetime of well. Regular production tests performed through a Multiphase Flowmeter equipment on each offshore platform is a first indicator to monitor the evolution of water production in a well. JM-X well has been experiencing water breakthrough since one week after initial perforation and WGR keep increasing following gas production decline. The strategy was initiated by conducting a bottom hole monitoring survey to identify water sources. Production Logging Tool (PLT) was used to precisely monitor pressure, temperature, water holdup, and fluid rate along the wellbore for further water source and production allocation analysis. Once the water source reservoirs have been identified, MWSO operation was requested. There are several types of MWSO equipment that are commonly used in Offshore Mahakam field each of which has selective economic consideration based on the expected well reserve. Considering operation difficulties and cost, MWSO program was made then will be monitored during the operation time to ensure the operation runs safely and smoothly. MWSO strategy on well JM-X was proven to be able to reduce water production from 900 bpd to only 20 bpd with a significant gain of gas production from 3 MMscfd to 9.2 MMscfd and oil production from 200 bpd to 750 bpd.
Formation Grain Size Profile Prediction Model Considering the Longitudinal Continuity of Reservoir Using Artificial Intelligence Tools
Authors: Liu Shanshan , Wang Zhiming 
Published: October 2021

Abstract
Grain size characteristics (d50, UC) of formation sands are crucial parameters in a sand control design. UC and d50 are commonly derived from sieve or laser particle size analysis (LPSA) techniques on a limited number of core samples in the process of drilling, which cannot represent the variations of grain sizes in the formation by the limited number of core samples. Moreover, staged and hierarchic design of sand control usually needs the whole longitudinal distribution profile of grain size. The grain size characteristics of the reservoir are formed in the process of a long history and have a good correlation with the formation environment of the sediments. Sand control design can only use test well data, because of lacking actual producing position cores. The vertical and horizontal anisotropy and heterogeneity of reservoirs bring difficulties and greater risks to the design of sand control schemes. Therefore, it is very important to find a simple and effective reservoir granularity prediction method.
The existing prediction models by artificial intelligence method use single point logging data as eigenvalues to predict d50 and UC without considering the longitudinal continuity of data. This paper presents an efficient solution to predict grain size profile based on conventional logging curves by using four machine learning method (ANN, Random forest, XGBoost, SVM). In order to make full use of the geological continuity of the reservoir, the longitudinal continuous points according to the spatial correlation are adopted as the machine learning feature parameters from the perspective of geological analysis and the data-driven grain size profile prediction model are established by using the logging curve trend and background information, which further improves the prediction accuracy of the model and provides basic data for sand control. The ANN model of five point mapping has the best prediction effect in predicting d50 with a highest correlation coefficient 0.819 and a lowest error MAE 9.59. The XGBoost model of five point mapping has the best prediction effect in predicting UC with a highest correlation coefficient 0.402 and a lowest error RMSE 1.15. This method has been successfully used in offshore oil field in sand control optimization.
How to Land Modern Data Science in Petroleum Engineering
Authors: Zhang Hongbao , Zeng Yijin , Liao Lulu , Wang Ruiyao , Hou Xutian , Feng Jiangpeng , Mulunjkar Amol 
Published: October 2021

Abstract
Digitalization and intelligence are attracting increasing attention in petroleum engineering. Amounts of published research indicates modern data science has been applied in almost every corner of petroleum engineering where data generates, however, mature products are few or the performance are not up to peoples’ expectations. Despite the great success in other industries (internet, transportation, and finance, etc.), the "amazing" data science algorithms seem to be challenged when "landing" in petroleum engineering. It is time to calmly analyze current situations and discuss the methodology to apply modern data science in petroleum engineering, for safety ensuring, efficiency improvement and cost saving.
Based on the experiences of several data products in petroleum engineering and wide investigation of literatures, the methodology is summarized by answering some important questions: what is the difference between petroleum engineering and other industries and what are the greatest challenges for algorithms "landing"? how could we build a data product development team? why the machine learning models didn't work well in real world, which are derived by typical procedures in textbooks? are current artificial intelligent algorithms perfect and is there any limit? how could we deal with the relationship between prior knowledge and data-driven methods? what is the key point to keep data product competitive? Several specific scenarios are introduced as examples, such as ROP modelling, drilling parameters optimization, text mining of drilling reports and well production prediction, etc. where deep learning, traditional machine learning, incremental learning and natural language processing methods, etc. are used.
Besides detailed discussions in the paper, conclusions are summarized as: 1) the strengths and weakness of current artificial intelligence should be viewed objectively, practical suggestions to make up the weakness are provided; 2) the combination of prior knowledge (from lab tests or expert experiences) and data-driven methods are always necessary and methods for the combination are summarized; 3) data volume and solution portability are the key points to improve data product competitiveness; 4) suggestions on how to build a multi-disciplinary R&D team and how to plan a product are provided.
This paper conducts an objective analysis on challenges for modern data science applying in petroleum engineering and provides a clear methodology and specific suggestions on how to improve the success rate of R&D projects which apply data science to solve problems in petroleum engineering.
Precise Cement Displacement with a New Cement Plug Tracking Method
Authors: Kabannik Artem , Korkin Roman , Demidov Demid , Fedorov Andrey , Khudorozhkova Aleksandra , Nieczkoski Micaela 
Published: October 2021

Abstract
During the primary well cementing operation, when the cement slurry is pumped into the annulus around the outside of the casing string, it is very critical not to over displace and let the displacement fluid enter the annulus.
Traditionally, to determine when to stop the cement displacement operation, the top cement plug position is tracked volumetrically by dividing the displaced volume by the casing internal cross-sectional area. However, the volumetric method is prone to uncertainties related to displacement fluid compressibility, high-pressure pump inefficiency, flowmeter inaccuracy, and variance in casing joint diameters.
The new cost-effective cement displacement monitoring method is based on the analysis of the pressure pulses generated by the top cement plug passing the casing. These pressure pulses are detected by the standard pressure transducer installed at the cementing head. When correlated with the casing tally, these pulses identify the plug position related to the completion elements that provide better accuracy than the volumetric method used conventionally.
The case studies include the successful cement displacement monitoring example and the case where the plug was prematurely stopped 90 meters above the landing collar, which was revealed by the subsequent drilling and confirmed independently by the new plug tracking method.
Optimization Strategies of Production Parameters to Prevent Hydrate Reformation in Marine Gas Hydrate Production System
Authors: Liu Zheng , Sun Baojiang , Wang Zhiyuan , Zhang Jianbo 
Published: October 2021

Abstract
In recent decades, the development of natural gas hydrates has become a research hotspot of scholars all over the world. However, the decomposed gas and water in marine gas hydrate production system may regenerate gas hydrates due to the low-temperature and high-pressure environment in seafloor. In this study, a transient temperature and pressure calculating model was established to predict the risk of hydrate reformation in production pipelines during offshore natural gas hydrate development. Using the proposed model, the region of hydrate reformation in gas hydrate production wells were predicted quantitatively. Meanwhile, the hydrate reformation management strategies through optimization of production design parameters in combination with hydrate inhibitor injection were proposed and discussed in detail. The results indicate that the risk of hydrate reformation is the highest in the drainage pipeline (DP); however, the flow in gas-water mixed transportation and gas production pipelines (MTP and GPP) basically does not satisfy the hydrate formation condition. In the process of production well design, adding additional the EH and ESP can fully eliminate the hydrate reformation risk in the DP without using the hydrate inhibitor.