bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Machine Learning-Based Data Augmentation Approach for Unconventional Reservoir Characterization Using Microseismic Data and EDFM
Authors: Leines-Artieda Joseph Alexander , Fiallos-Torres Mauricio Xavier , Alharthi Amena , Mahmoud Salah El din , Al Hashmi Abdulla , Alqaydi Maryam , Ramsay Travis , Gong Yiwen , Yu Wei , Miao Jijun , Escorcia Alvaro , Useche Franklin , Al Bannay Aamer , Fonseca Rahul-Mark , Sepehrnoori Kamy 
Published: October 2022

Abstract
Multi-stage hydraulic fracturing has recently gained strong interest in unconventional plays in the Middle East due to high natural gas production potential. However, prevalent characteristics of the area, including high-pressure / high-temperature (HPHT) conditions and presence of complex natural fracture networks, pose significant challenges to reservoir characterization. These challenges have motivated the development of an integrated workflow using microseismic data for the characterization of reservoir properties resulting from the interaction between natural and hydraulic fractures.
This study proposes a reliable method for modeling hydraulic fractures from scarce microseismic data. Initially, a microseismic model—based on field records of microseismic data and natural fracture spatial characterization—was developed. Issues related to limited microseismic data availability were tackled through combination of a probabilistic algorithm, Gaussian Mixture Model, and a DFN model. Then, the resulting synthetic microseismic events enabled the generation of a hydraulic fracture model using the embedded discrete fracture model (EDFM) and an in-house microseismic spatial density algorithm that captured major hydraulic fracture growth tendencies. Next, the created hydraulic fracture geometries were validated against a physics-based hydraulic fracture propagation model. Lastly, a single-well sector model—based on a corner point grid that honored the original 3D discrete fracture network (DFN)—was history matched, confirming the successful application of the proposed methodology.
Applying Blockchain Technology to Process Safety with Focus on Integrity Management for Oil and Gas Industry
Authors: Laskar Sandipan 
Published: October 2022

Abstract
Disruptive innovation has extensively changed human interactions, market trends and other aspects such as transportation and communication. Over last decade many new technologies (artificial intelligence, block chain technology, Internet-Of-Things) has changed the way world do business. These new technologies are playing an important role in increasing efficiency and effectiveness in businesses around the world. This paper focuses on application of blockchain technology and use of distributed ledgers to leverage process safety and integrity management program.
The focus of safety management system is to evaluate hazards and decrease risks with an assurance that the identified safeguards are effective. All the elements of process safety management program are interlinked so failure or gap in one element will adversely affect the efficacy of the overall process safety management program. The effectiveness of a process safety management depends on many factors such as management support, personnel participation, effective and tamper-proof data management and reporting. So, it takes considerable number of resources to set up and manage these systems. This paper explores use of blockchain and distributed ledgers to effectively manage elements of process safety management program with major focus around integrity management for oil and gas industry. The paper focuses on core 6 elements however the discussion could be applied for the other elements of the process safety management program. This paper provides an overview application of blockchain to different elements of process safety management program discussing the pros and cons. The paper provides a detailed scoping of application of blockchain and distributed ledgers for integrity management program.
Predicting ESP failures Using Artificial Intelligence for Improved Production Performance in One of the Offshore Fields in India
Authors: Devshali Sagun , Tripathi Shailesh Kumar , Dodda Dhiraj , Kumar Manish , Uniyal Rishabh , Yadav M. , Malhotra S. 
Published: October 2022

Abstract
Field X is situated at a water depth of 90 meters in the western continental shelf at a distance of 200 Kilometers from Mumbai. It is one of the few fields in the world operating entirely on Electric Submersible Pumps with 36 wells in 5 wellhead platforms producing 62907 barrels of liquid per day with an average water cut of 68%.
The performance of ESPs is being continuously monitored in the field. With continuous improvement, the run life of ESPs has increased from a few months to an average of 3 years. Despite the improvement in the run life, unexpected failures still occur from time to time. These unanticipated ESP failures cause substantial production deferment leading to considerable losses in terms of revenue and resources. This paper presents the findings of an Artificial Intelligence based model developed for failure prediction of ESPs aiming to minimize the unexpected production loss for Field X.
From the historical data obtained, 47 instances of pump failure have been identified. One of the challenges encountered during Data Exploration was missing data which in many cases was due to downhole sensor failure before the pump failure. The missing values have been inferred and imputed from the known available parameters for each pump. Various machine learning algorithms including Random Forest Regressor, Xgboost Regression, Copula-based Outlier Detection, Scalable Unsupervised Outlier Detection and Long Short Term Memory (LSTM) Autoencoder have been applied on these failure instances to develop a model for predicting the run lives of ESPs. Out of all the methods, LSTM Autoencoder model has been found to be the best suited model for anomaly detection before failure of ESPs. Autoencoders learn patterns in data over long sequences which makes them suitable for anomaly detection before the actual pump failure. The pattern recognition algorithms of Autoencoders have been able to predict the anomaly at approximately ~60 days before failure in a number of pump failure instances.
The paper discusses a proactive approach by building a predictive model for estimating ESP lifespan based on machine learning algorithms. The model's predictive accuracy can be improved over time by adding information and further improving the model components.
Transforming Well Planning through an AI-Assisted Well Trajectory Optimization Approach Applied to an Offshore Field in the Middle East
Authors: Almarzooqi Laila Zayed , Fonseca Rahul Mark , Buhindi Hani Ahmed , Binabadat Ebtesam Khaled , Al Hamlawi Imad , Dolle Norbert , Meshcheriakova Olga 
Published: October 2022

Abstract
This paper describes the use of Artificial Intelligence (AI) to support well planning in an Abu Dhabi offshore field. In this application, AI has been used for automated and unbiased evaluation of well trajectories, with the objective to optimize the cost, risk versus value trade-offs while considering complex issues such as anti-collision with existing wells.
A Rapid Random Tree (RRT) algorithm, well known for applications in robotics, has been used to generate well trajectories for 2 actual drilling projects. The algorithm creates a full and unbiased option space of feasible well trajectories, presented in a custom-built and collaborative digital solution.
Results demonstrate that AI-generated well trajectories were 2-5% shorter than manually planned and/or actual drilled wells. This use case also shows that an AI can design thousands of possible well trajectories in only a few hours, adhering to well design rules and anti-collision constraints. This would lead to significant time savings, and possibly material drilling cost reductions, in even more congested brownfield assets.
This paper describes a real application of AI-assisted well trajectory planning in an operational setting, with a comparison to manually planned and actual drilled wells. As such, this provides a rather unique insight into the business value-adding potential of Artificial Intelligence in traditionally manual work processes.
Remote Digital Technologies Driving Innovation - A Case Study on Subsea Operations
Authors: Bell Maxine , Sriskandarajah Anojan , Heaulme Victor , Bouillouta Fouzi , Seinuah Alex 
Published: October 2022

Abstract
The energy industry operates in a world where a consistent connection over real-time information is expected, however, less than 1% of available data sets are currently leveraged in the industry and usually – when monitored – most of the evaluations are based on human assessment of raw data. Today's business complexity requires an evolution in the way we share project and service information in the energy industry – from a reporting culture to true transparency and collaboration. There is a need for a digital system able to embrace raw data, perform analytics, and display key recommendations and insights. This will play a key role in turning challenges into opportunities.
Adopting a real-time modular platform for proactive asset management and instant troubleshooting can be leveraged during any stage in a field's life cycle. Built for collaboration, the platform unlocks value through minimizing equipment downtime, enabling proactive servicing and digitally optimizing the understanding of equipment needs throughout the life of field. This agile approach consolidates multiple data streams into one trusted digital source including:Autonomous monitoring of subsea wells, wellheads and xmas tree integrity without the need for active power. Designed to integrate with existing subsea infrastructure, these battery-operated monitoring systems passively monitor for a breach of material and will alert the operator of any integrity issues.A fully equipped remote communications tool that goes beyond simple audio and video chat to support effective remote support.Integrated visualization of robotic cleaning and inspection data of subsea equipment.Objective view of the overall health status asset(s), field(s) or infrastructure, considering those elements which have the greatest bearing on health and longevity, with the possibility to monitor health trends over time, prolong operable life and optimize productivity.
The implementation of the right tool, feature-rich, intuitive, and easily scalable leverages data and analytics to unlock a collaborative model of proactive servicing, prolong asset operable life and reduce OPEX. The case study demonstrates the positive outcomes that can aid the decarbonization of monitoring operations in remote locations. This approach can yield exceptional cost savings when compared to traditional methods of on-site visual monitoring. A wider overview will include the benefits of extending the window for full intervention or P&A, including lower asset risk, and protecting the environment.
This paper illustrates deployment examples of the platform on subsea fields in the North Sea and Asia, its value, approach, as well as recommendations for any operator willing to embark on a similar journey.
Machine Learning Based Prediction of PVT Fluid Properties for Gas Injection Laboratory Data
Authors: Ghorayeb Kassem , Mogensen Kristian , El Droubi Nour , Ramatullayev Samat , Kloucha Chakib Kada , Mustapha Hussein 
Published: October 2022

Abstract
Gas injection pressure-volume-temperature (PVT) laboratory data play an important role in assessing the efficiency of enhanced oil recovery (EOR) processes. Although typically there is a large conventional PVT data set, gas injection laboratory studies are relatively scarce. On the other hand, performing EOR laboratory studies may be either unnecessary in the case of EOR screening, or unfeasible in the case when reservoir fluid composition at current conditions is different from initial conditions. Given that gas injection is to be widely assessed as an optimal EOR process, there is increased demand on time- and cost-effective solutions to predict the outcome of associated gas injection lab experiments.
While machine learning (ML) is extensively used to predict black-oil properties, it is not the case for compositional reservoir properties, including those related to gas injection. Can we use the typically extensive conventional laboratory data to help predict the needed gas injection parameters? This is the core of this paper.
We present an ML-based solution that predicts pertinent gas injection studies from known fluid properties such as fluid composition and black oil properties. That is, learning from samples with gas injection laboratory studies and predicting gas injection fluid parameters for the remaining, much larger, data set.
We applied the proposed algorithms on an extensive corporate-wide database. Swelling tests were predicted using the trained ML models for samples lacking gas injection laboratory data. Several ML models were tested, and results were analyzed to select the most optimal one. We present the algorithms and the associated results. We discuss associated challenges and applicability of the proposed models for other fields and data sets.
Green Screen Application Digitalized Approach for the Environmental Management System (EMS)
Authors: Abubakr Ahmed Abdulraheem , AlGhamdi Abdullah Omar 
Published: October 2022

Abstract
As part of a comprehensive investigation, this study has reviewed the companies adopting digital technologies to establish an ISO 14001 Environmental Management System (EMS). Organizations have implemented digital strategies for environmental management systems (EMS) to ensure that environmental elements are continually improved. Green Screen is a real-time Business Intelligence (BI) dashboard designed to digitalize the Environmental Management System (EMS) providing a holistic overview of the department's Environmental performance and coming to an alignment with the global trend to deploy IR 4.0 technologies to digitalize operations. The solution provides real-time information on the Environmental Management System (EMS) significant aspects that consists of Climate, Water, Land, and Decarbonization.
The dashboard is meant to aggregate data from multiple sources connected to the organization's Environmental Aspects, evaluate it according to the defined KPI formula, and then show the needed information in a user-friendly dashboard to provide insights into the organization's performance. The Green Screen dashboard uses many tools to collect, analyze, and present data relating to the department's environmental aspects. The system was carefully designed to boost the overall department Environmental Management System (EMS) by providing critical insights, such as KPI exceeding and emissions offsetting, to all stakeholders and customers. These insights are grouped based on the organization's EMS. The Green Screen will positively impact the overall environmental performance by the following: Achieving the full implantation in EMSEliminating around 24 working hours per month needed for monitoring.Providing the stakeholders and customers with an easy interactive monitoring toolDriving proper corrective actions and adjustment to under-performing areasServing as an online, easy, and fast access reference to all the department's EMS
The report also discusses the factors that influence the acceptance of environmental certifications by organizations, particularly in developing nations.
Digital Twin Application for Boosting Oil Production, Predictive Analytics of Asset Integrity and Mid-Term Forecasting of Field Performance
Authors: Vorobev Ilya , Koshkin Timur , Prokopev Mikhail , Rakhmangulov Yulay , Kombarov Semyon 
Published: October 2022

Abstract
There are often situations when readings of a large number of sensors installed at the field are used as needed, rather than on a systematic basis. The paper is devoted to the experience of building and using a Digital twin (DT) of the oilfield, history matched using instrument readings at the main nodal points from the well bottom to the tank farm. Digital twin is used to solve practical problems of selecting measures to increase production, specifying the maintenance program and chemical injections, overall allocating production by wells without direct measurements of production flow and forecasting in different time horizons the entire field production.
Enhanced Oil Recovery at Zero Cost. Will Cyclic Waterflooding Breath a New Life into Mature Oil Fields? A Case Study in South Kazakhstan
Authors: Mukanov Adil , Kudalsha Aimurat , Kassenov Duman 
Published: October 2022

Abstract
South Kazakhstan is one of the hydrocarbon rich provinces in Kazakhstan, which is among the top 20 oil producing nations. However, most of the big fields of the basin are entering the last stages of development. Consequently, operators look for opportunities to maintain economical production by applying IOR methods at lower costs. Hence, cyclic waterflooding was considered a good candidate because of no expenditures. The paper demonstrates the main stages, mechanisms, and results of the project.
The schematic of cyclic waterflooding is carried out by changing injection rates in phases. This leads to fluid flows redistribution in heterogeneous reservoirs due to capillary forces. So, the field trial was implemented on the KK field, which is represented by stratified sandstone mostly waterflooded. The injection pattern is irregular and eight patterns participated in the project. Rates varied from 50 t/d up to 300 t/d. Phases consisted of one group of injectors introducing water at lower and another group at higher rates with one-month duration. After that, the wells’ rates were adjusted.
Cyclic waterflooding has been in operation for almost a year. During that period, producers’ working modes haven't been changed. As the result of observations, some wells have moderate or even no impact, but some wells exhibited remarkable improvements, e.g. a producer which has three injection wells around experienced watercut decrease from 90% up to 40%, while oil rate increase from 1 t/d up to 5 t/d. Overall, oil production from a group of nearby producers whose oil rates were previously reducing with a 41% decline has a clear trend of full stabilization. The main aspect causing this phenomenon is watercut decrease. The parameter fell from 95% to nearly 86%. Thus, it became beneficial not only in terms of oil production but also surface infrastructure. Less water volume treatment is required and it is critical for gathering system under conditions of growing water production.
Summing up all the above-mentioned cyclic waterflooding has big success in our field case study. The initial modest forecast turned to significant oil increment and watercut decrease. Moreover, the company saved money which makes this IOR approach the most preferable to implement. Therefore, additional two projects recently started on other similar fields. It is obvious that if properly managed, improved oil recovery is possible at no cost breathing new life into old fields.
Predictive Analytics for Third Party Threat and Geohazard
Authors: Ahmad M Nazri , Ali Napiah M Nazmi M , Koi Swee Ling , Idris Naila Rusma 
Published: October 2022

Abstract
Third party encroachment and geotechnical hazard are two major threats for onshore pipeline system and managing them is a real challenge to pipeline operators including PETRONAS. If these threats are not properly managed, the consequences may lead to major catastrophe which impacts public safety, asset damage, financial loss and tarnish company's reputation.
Aerial surveillance i.e. drone or helicopter has been widely used by pipeline operators for inspection and monitoring of 3rd party threat and geotechnical hazard. However, the data produced from these inspections are in the forms of images and videos resulting to massive datasets. Manual interpretation of these data has caused lengthy processes, time consuming, costly, and prone to human error during data analysis. Motivated from the above pain points, a predictive analytics tool was developed in-house using various machine learning technologies to automate anomalies identification captured during aerial inspection as part of monitoring and risk management along pipeline right-of-way (ROW). The outcome from the predictive analytic tool has assisted pipeline operator to perform early intervention of possible pipeline failure due to third party and geohazards. Data automation has significanly reduced data processing time and human error for anomalies detection and verification, this subsequently improve workforce productivity while reducing safety risks, operations and maintenance costs.
Selective and Reversible Water Shut-Off Agent Based on Emulsion System with Nanoparticles Suitable for Carbonate Reservoirs at High Temperature and High Salinity Condition
Authors: Abe Masashi , Furuno Jumpei , Murakami Satoru , Sonoda Takashi , Sergeev Vitaly , Tange Masaaki , Hiraiwa Takeshi , Yonebayashi Hideharu 
Published: October 2022

Abstract
This paper presents the evaluation results of water shut-off (WSO) agent based on emulsion type chemical material with nanoparticles. The WSO agent called Emulsion System with Nanoparticles (ESN), it has several unique advantages to existing polymer and gel materials; high thermal stability, low sensitivity to mineralization, thixotropic characteristic, selectivity of blocking effects for oil and water, and reversibility of blocking effect. In WSO application, these properties of ESN could be a good match for the tasks of improved oil recovery. Also, the surface modified silica (SiO2) nanoparticles have an important role to drastically enhance stability of the emulsion system. The ESN can be prepared easily by mixing emulsifier and silica nanoparticles with the on-site oilfield materials such as crude oil and brine. The refined diesel oil can be used for ESN preparation instead of crude oil, so it will be an option depending on the situation of target oil field infrastructure and operator's policy. To evaluate the performance of ESN as WSO agent for UAE's carbonate reservoirs, core flooding tests using low permeability carbonate cores with different water saturation were performed at high temperature condition. The ESN, which is confirmed to be stable at 120 °C and 240,000 ppm TDS, achieved 85 % permeablity reductions for intermeaiete and high Swi cases of the core flood tests. The relative permeability analysis confirmed unblocking by oil inflow for intermediate and low Swi cases. Through comparative analysis and discussing these test results, the laboratory study investigated technological potentials of ESN to block water zones and control water cut of oil wells. This paper introduces the detailed evaluation results of ESN and possibility to successfully apply it for UAE's oil fields in the future.