bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Data-Driven Approach Enhances Conventional Reservoir Surveillance Methods for Waterflood Performance Management in the North Sea
Authors: Jahangiri Hamid Reza , Adler Carwyn , Shirzadi Shawn , Bailey Richard , Ziegel Eric , Chesher James , White Matthew 
Published: June 2014

Abstract
Understanding and quantifying relative connections among injectors and producers is a key to improving reservoir performance under waterflood. Inter-well tracers were used to assess injector-producer connections in early days of development. A data-driven approach is a valuable process for estimating injector/producer connectivity as more wells are drilled. It offers a viable alternative to costly and challenging tracer operations in offshore environments such as the North Sea.
BP’s Field of the Future Technology Flagship has developed an innovative approach for waterflood performance, Top-Down Waterflood (TDWF) Diagnostics and Optimization. This novel approach combines the Capacitance Resistivity Model (CRM) and BP’s proprietary event detection and association technology to quantify the connections between the injectors and the producers.
This paper compares the use of inter-well tracer and streamline model with TDWF in a BP operated field (North Sea), using the value of injection water (VoiW) as the outcome of connectivity. Initially, the paper demonstrates evaluating the sensitivity of various CRM parameters, as well as selecting periods in a field’s history for comparison with tracer breakthrough data and streamline model results. The comparison to streamline and tracer results is made based on TDWF VoiW with no prior information on connectivity. For selected periods, CRM results mostly agree on connectivity with tracer and streamline results. Using injector events and producer responses (rate, pressure), we provide prior information to CRM which enhances the confidence in TDWF VoiW estimates and results in better consistency with tracer/streamline interpretations.
The best strategy for enhancing waterflood performance management is the integration of the TDWF methodology with existing workflows, such as tracer and streamlines modelling. As a direct result of this study, TDWF will be used as a "virtual" inter-well tracer. The efficiency of the newest injector in the field then can be understood better without the need to conduct a tracer program.
A Data-Driven Approach to Modelling and Optimization for a North Sea Asset Using Real-Time Data
Authors: Ziegel E. , Shirzadi S. , Wang S. , Bailey R. , Griffiths P. , Ghuwalela K. , Ogedengbe A. , Johnson D. 
Published: June 2014

Abstract
Increased numbers of sensors on wells do not result in increased production. New methods of data analysis are needed to capitalize on the new data. A novel approach to modelling gas coning has been developed for one of BP’s assets. It integrates several data-driven models representing different aspects of a well’s performance.
Prior art, as exemplified by other approaches in BP and other case studies in the literature, is discussed. A description of the field and its operational issues around gas production is given. The details of the methodology that has been used are provided. The novelty relative to the prior methods described in literature is discussed. Advantages of the data-driven approach are explored, incorporation of the models into real-time operational support systems is considered, and other opportunities for leveraging available data to maximize the value from our operations are mentioned.
The approach has been tested on a North Sea reservoir. An interconnected suite of models for reservoir pressure, indication of the presence of gas coning, virtual flow measurement for fluids and gas production, and dynamic prediction for future fluids and gas rates, was developed. The suite of models predicts fluid and gas rates for use in short-time-loop optimizations. Models use the available real-time data, which includes bottom-hole pressure, wellhead pressure, choke changes, and gas lift rate. The data is captured for production, injection and observation wells.
The methodology assists in the prediction of the dynamic well behavioral changes which result from the onset or occurrence of gas coning. It enables the asset to leverage its real-time sensor data to manage and optimize its wells while respecting the facility’s constraints. It works for similar assets where dynamic well behavior must be managed.
M2M Field Communications: Keeping Workers and Key Systems Connected
Authors: Lewis Kenneth , Burt Michael 
Published: June 2014

Abstract
Many businesses and industries have seized upon the opportunity to expand the ability to command, communicate and control remote systems, processes and operations. Within the Critical Infrastructure Industry (CCI), Process Control Network (PCN) systems are integral components of the operations for the oil & gas industry. The use of Machine To Machine (M2M) technologies in the PCN environment is not new but it has continued to advance. From the early days of collecting basic telemetry data M2M has continued to evolve into highly sophisticated and advanced applications providing information for critical decision support systems.
One of our initiatives has been to focus on developing the potential to enhance the M2M communications infrastructure capabilities through the use of cellular services. Working with our preferred M2M systems integration alliance partner, we are piloting solutions to leverage the cellular services offered by Wireless Carriers to implement private cellular networks.
Through our innovation projects, we have been able to reduce the time to deploy and cost to maintain field communications and increase the productivity of mobile workers by providing reliable communications connectivity. We have established a consistent M2M technology governance model for the enterprise which provides an alternative field communications and data backhaul solutions for challenging operating environments.
The ability to develop a cost effective method to deploy cellular based sensors provides the opportunity for the collection of additional relevant data and trend analysis in support of decision support systems. This increase in available data can directly impact the decision quality of all economic and operational production business decisions.
Samarang Integrated Operations (IO) - Begin To Deliver Value
Authors: B Wan Mamat Dato’ Wan , Sayung Colinus Lajim , Ahmad M Zul , B Hashimi M Hafez , Wong Lee Hin , Muhamed Salim Muzahidin , Mohd Som Mohamad Kasim , Kurniawan Rudy , Biniwale Shripad 
Published: June 2014

Abstract
Samarang field Integrated Operations (IO) initiative was incepted in year 2011 as part of the field redevelopment plan. The objective is to enable real time production, reservoir and process surveillance and optimization in order to gain reservoir and production understanding for effective asset management. The project is planned to complete through 2016 with 4 major milestones as follows: Milestone 1: Design and PlanningMilestone 2: Surveillance and monitoring with collaboration between the offshore and onshore operation officeMilestone 3: Production and reserve recovery optimization with tri-node Collaboration Working Environment (CWE)Milestone 4: Total asset optimization with hybrid steady-state and transient-state simulation
The project footprint has reached the Milestone 2 and has been developing and deploying operational capabilities for improved asset management of Samarang. It is managing 10% of Samarang field production at this stage and is aiming at delivery of 100% field production by the end of year 2014.
We reported the progress, lesson learned and benefits of Milestone 1 delivery in year 2013 Intelligent Energy Conference and this paper is intended to further describes the growing track record of value delivery, achieved through the deployment of Milestone 2.
Possibilities of Using Wired Drill Pipe Telemetry During Managed Pressure Drilling in Extended Reach Wells
Authors: Siahaan Hardy B. , Bjørkevoll Knut S. , Gravdal Jan E. 
Published: June 2014

Abstract
Extended-reach wells with narrow pressure margins are complicated with respect to maintaining accurate pressure control during drilling. The high compressibility of the drilling mud that is typically used in such wells result in large pressure transients that need to be handled by the Managed Pressure Drilling (MPD) system. Also, limitations in sampling interval and delay in transmission time for downhole pressure measurements using mud pulse telemetry affects the ability to use the data.
This paper presents results showing the benefit of using wired drill pipe for transmitting pressure measurements when drilling with back-pressure MPD in an Extended-Reach Drilling (ERD) operation. It is shown how minutes can be saved each connection by using wired drill pipe in combination with state-of-the-art MPD control algorithms. It is also shown how a combination of traditional back-pressure MPD, and wired drill pipe can significantly improve the ability to control down hole pressure. Measurements are generated synthetically by an advanced transient well flow model. The example cases are based on a real ERD well drilled recently in the North Sea.
Benefits by this approach are the ability to minimize downhole pressure variations that will occur in ERD wells and, in particular, save time during connections, and also more accurate pressure control, and thereby safer drilling operations.
A New WITSML/PRODML Data-Object to Handle Completions and Well Services for the Life of a Well
Authors: Aydelotte R. , Prather F. , Hugot A. , ºLucas-Clements D. , Mao M. , Magsipok J. , Marcotte D. , ºOrmerod L. 
Published: June 2014

Abstract
The new Completion data-object is an XML specification for describing a well completion throughout its life. It is part of the WITSML and PRODML industry data-exchange standards. Using the object, well completion data can be accurately, reliably, and consistently exchanged between the many software applications used to plan, manage, and record work on a completion over a well’s life. This paper describes the business case and value of the Completion data-object, explains the solution, and presents details and examples about how it can be used and implemented.
Over a well’s life, the completion configuration is typically changed many times. This work involves changes in operational "ownership" between different companies (e.g., operator and service companies) and different departments (e.g., drilling and completions, production operations, and workover teams). For safer and efficient operations, it is vital that the team taking over knows the current completion configuration. Drilling and operations organizations use different software systems from different vendors. The transfer of completions data between these systems can be a very manual process, involving reformatting or even re-entering all data into the new system—which is inefficient and introduces opportunities for errors, jeopardizing safety and schedules.
The use cases addressed by the new Completion data-object support specific workflows and requirements, including: "Snap Shot" of a completion at any instant, supporting "what is the current status?"; "Change Log" representing events and changes to a completion between one time and another, supporting "what has changed?"; and "Cumulative History" for all data over the completion’s life for synchronization of databases, supporting "give me all the information".
Thus the business drivers for using this data-object include: improving safety, efficiency, data consistency, accuracy, and quality; eliminating multiple data entries; giving faster access to the required data; and reducing reliance on paper well files and spreadsheets.
Production Data Management and Surveillance in Shale Operations
Authors: Farid Kemal , Bhatti Kashif , Dassinger Janet , Fudickar Ben , Fuge Clara , Gartrell Kimberly , Kristensen Eivind L. , Kuzma Heidi , McLaughlin Sharon , Nichols Dana 
Published: June 2014

Abstract
Production Data Management and Surveillance in Shale Operations presents both the benefits and challenges of field data management in shale production operations. The examination of the lifecycle of field production data the paper offers will lead us to explore issues of surveillance, predictive analytics, enterprise data availability, data quality, integration, and regulatory compliance from the perspective of unconventional resource operators. The paper also includes case studies that detail the implementation and lessons learned from five shale oil and gas producers.
Factory drilling in unconventional plays leads to highly paced operations and a large number of wells, which in turn leads to the generation of an enormous amount of field data that must all be captured, processed and turned into actionable information for surveillance, operational accounting and HSEQ compliance. This data, much of which is collected by field personnel at the well site, includes fluid volumes and operating conditions as well as run tickets, tank battery inventories, sales volumes, equipment status, and chemical usage. Also, increased sensitivity to environmental issues around shale plays requires operators to closely manage water, emissions and other environmentally impactful measurements, all of which must be collected, monitored and reported.
Maintaining the quality of the gathered data is paramount as poor data can lead to costly consequences, from under- optimized production to fines for inaccurate reporting. Integrating surveillance processes with the field systems will promote consistency and accuracy. In conjunction, tools that provide asset-specific variances, alerts and visualizations will help identify operational issues immediately, allowing for swift alignment of the field team with corporate goals. Raw production measurements collected in the field are processed to produce allocated production volumes at each well and zone. These allocated volumes are then put to use by different departments across the operator’s enterprise, in their planning, forecasting, operations management, revenue accounting, marketing, regulatory and partner reporting.
Field information and production data lie at the heart of operations management for all operators. Production Data Management and Surveillance in Shale Operations submits multiple case studies on the collection, analysis and distribution of this data, along with the best practices employed in shale plays.
Factors Affecting Implementation of Global Remote Operations
Authors: Thorsen Arve K. , Undheim Magne , Ferrero Ricardo 
Published: June 2014

Abstract
Remote operations are fully available today with the current technology for data storage and distribution. There are multiple industry examples of operations performed remotely, without adopting new standard workflows on a global scale.
This paper examines elements that have been developed for remote operations, from the rigsite data aggregation and distribution through the real-time analysis and feedback into operations. Two main topics are covered: current functional remote operational processes, such as reservoir navigation (geosteering) and drilling optimization. Secondly are areas where change to remote operation is possible through current technology, but is not implemented.
There are many reasons for not implementing complete remote operations, and often depend on geography and company strategy. Areas (described in the paper) where there is reluctance to change are divided into the following categories: -Oil field culture-Company strategy-Understanding and trust in technology-The human factor; reluctance to change-Collaboration and change in work processes between companies-Financial aspect; initial investment and return on capital-Legal aspect; data security and accessibility to experts on a global basis
The paper examines each element’s ability to increase the speed of implementing into the digital oil field, followed by suggestions and possibilities in overcoming each one of them.
The paper evaluates oil field areas where remote operations are successfully implemented with parallels to areas where the implementation is not in place, focusing on solutions for further development of the digital oil field from current successes.
Ideas are proposed for developing and increasing the use of remote operations.
Automated Geomechanical Analysis from Remote Operational Centers
Authors: Bartetzko Anne , Wessling Stefan , Dahl Thomas , Thorsen Arve K. 
Published: June 2014

Abstract
One of the larger challenges in the well construction phase in exploration drilling and field development is understanding the stress regime and rock stability in the various geological layers. Great focus centers on the reservoir for obvious economic reasons, although most of the wellbore construction cost occurs above the reservoir, mainly driven by section length and sparse information on vital parameters for geomechanical analysis.
There are two main points in a cost-effective geomechanical analysis: it must be performed without increased cost or delays in well construction, and the analysis must be accurate and results fed back to the wellsite for implementation.
Employing appropriate, high-quality measurement devices in the drillstring is important to verify geomechanical models and adjust them to new information such as unintentional drilling operational challenges that can be used to constrain the pressure window. Geomechanical events must be identified before developing into a non-productive time situation. Continuously monitoring and analyzing a geomechanical event ensures correct measures are employed to mitigate problems. A subject matter expert requires the appropriate technology to highlight the optimal analysis processes.
This paper describes possible events of borehole-related collapse and how the automated process can detect wellbore stability issues. Utilizing remote operations to capture event details and deliver solutions for procedures that prevent or terminate the deficient circumstances enables an operation to continue without increased risk or delay.
The communication with the operational team is vital, and all work processes and command lines must be clear and followed. Personnel training and workflows are covered in the paper.
Advanced Well Structures: An Artificial Intelligence Approach to Field Deployment and Performance Prediction
Authors: Enyioha Chukwuka , Ertekin Turgay 
Published: June 2014

Abstract
Advanced well structures present themselves as a veritable tool for exploiting unconventional resources. These well systems reduce surface footprint of drilling operations whilst enhancing production by increasing contact area with the reservoir. Traditional methods for well performance analyses barely capture the complex interaction between these well structures and the reservoir. Therefore, this paper discusses the application of artificial neural network models to forecast production from advanced well structures, and well designs for field deployment of these well structures in unconventional multi-phase reservoirs.
Two forward-acting models and two inverse-acting models were developed. The forward-acting models predict oil, water and gas production profiles (and bottom-hole pressure profile for flow rate-specified wellbore conditions) for any given well design with parameters within the range of values used in training the model. The inverse-acting models generate well designs that can meet a desired oil cumulative production profile. Gas and water cumulative production, and/or bottom-hole pressure profiles are also predicted by the inverse models. For each category, one model was developed for constant pressure wellbore conditions, while the other model was developed for flow rate-specified wellbore conditions. Predicted well designs were validated using high fidelity simulators.
Synthetic field data was used for training, which were drawn from a 574-acre, isotropic and homogenous, naturally fractured reservoir system that represents average reservoir characteristics. Matrix permeability was 0.01 mD while fracture permeability was 5.0 mD. This reservoir system was utilized for each model. Well design parameters include the length and location of the horizontal mainbore; number of side laterals; length, spacing, and direction of each lateral.
The developed models showed good performances with minimal prediction errors. These results are promising, lending credence to the application of artificial intelligence for even more complex reservoir systems. The observed results should also boost confidence and interest in the use of advanced well structures for field applications.
Smart Fields-10 Years of Experience in Intelligent Energy and Collaboration
Authors: Chai Chon Fui , van den Berg Frans , Engbers Peter , Sondak Glenn 
Published: June 2014

Abstract
Shell’s Smart Fields programme has built elements of Intelligent Energy into the key field developments over the past ten years. Considerable value has been achieved, from new technologies, new ways of working and global implementation. Various technical solutions have been deployed and new ways of working pursued. New fields in development go through a screening, to identify intelligent energy elements valuable to their project. High value has been achieved from solutions in production surveillance and optimisation, smart wells, real time monitoring and optimisation and time lapse reservoir surveillance. As we progressed in our Smart Fields journey, the focus of the Smart Fields programme has shifted from "selling" individual solutions to achieving the "Appropriate Level of Smartness" (ALoS) in all our key projects & fields.
Integration and new ways of working are required to be successful. Collaborative Work Environments (CWEs) have been implemented on a large scale, mostly focusing on the production and field surveillance area and on the real time drilling monitoring. CWEs have improved the cooperation across the boundaries between disciplines and between office and field locations, enabling faster and better decision making. Strong focus has been placed on embedding the changes into the organisation. Business value is achieved only when solutions and new ways of working are adopted and maintained.
Detecting and Correcting Pipeline Leaks before they Become a Big Problem
Authors: Cramer Ron , Shaw David , Tulalian Robert , Angelo Pabs , van Stuijvenberg Maarten 
Published: June 2014

Abstract
Timely pipeline leak detection is a significant business issue in view of a long history of catastrophic incidents and growing intolerance for such events. It is vital to flag containment loss and location quickly, credibly and reliably, for all green, brown field critical lines in order to shut down the line safely and isolate the leak. Pipelines are designed to transport hydrocarbons safely; however, leaks have severe safety, economic, environmental and reputational effects.
This paper will highlight robust, reliable and cost effective methods, most of which leverage real time instrumentation, telecommunications, SCADA, DCS, and associated online leak detection applications.
The purpose of this paper will be to review the underlying Leak Detection business issues, catalogue the functional challenges and describe experiences with available technologies. Internal and External techniques will be described, including basic rate of change of flow and pressure, compensated mass balance, statistical, real time transient modeling, acoustic wave sensing, fiber optic cable (distributed temperature, distributed acoustic sensing) and subsea hydrophones. The paper will also describe related credibility, deployment, organizational and maintenance issues with an emphasis on upstream applications.
The scope will include leak detection for pipelines conveying various flowing fluids – gas, liquid and multiphase flow. Pipeline environments will include subsea and onshore.
Advantages, disadvantages and experiences with these techniques will be described and analyzed.