bluuebunny/crossref_metadata_2025 huggingface dataset:
Geographically-Distributed Databases: A Big Data Technology for Production Analysis in the Oil & Gas Industry
Authors: Preveral Aymeric , Trihoreau Antoine , Petit Nicolas
Published: June 2014

The paper discusses some reported shortcomings of state-of-the-start IT technologies currently employed in the data management of Oil & Gas production operations. Most current IT infrastructures connect historian databases, production databases and application servers. This creates complex issues of data consistency between these systems. In the discussion, a particular focus is put on the geographically-distributed nature of the network which suffers from low-bandwidth limitations and un-reliabilities, e.g. due to satellite communication links.

Taking the production engineers’ viewpoint, an example of production allocation using Data Validation and Reconciliation (DVR) serves to stress the malicious impacts of the described architecture. Production allocation represents one of the various monitoring and analysis tasks that are performed, on a daily basis, at the centralized level of data management systems. A quantitative study shows that the problem of mis-synchronization of databases is of great practical importance.

We propose solutions to improve the robustness to communication outages. To improve data consistency across sites in a decentralized manner, the paper exposes the key concepts of distributed storage, message-based communication, and clustering. More generally, the paper proposes to shine a light on the potential relevance of several recent advances in the scientific field of "big-data" to the world of Oil & Gas upstream industry. These off-the-shelf technologies must be specifically tailored to geographically-distributed networks. The specificities are detailed, the necessary development work is outlined, and the potential qualitative benefits are estimated. A possible implementation is sketched.
From a Daily Drilling Report to a Data and Performance Management Tool
Authors: Dawson Colin , Verkuil Harry
Published: June 2014

Using historical data to aid the planning of future operations is a very promising area of data analytics. Many organisations have their own data reporting tools alongside a number of benchmarking tools for use in performance analysis. One commonly used tool for both purposes are locally installable spreadsheet packages. In using this type of tool, the ability to analyse data and communicate with other applications is very limited. This limitation results in less accurate performance analysis, which leads to inaccuracy and less detailed analysis.

Using a number of different methods/tools for the same purpose is a problem that is heavily embedded in today’s Oil & Gas culture. This not only doubles or trebles costs, but leads to a sparsely populated and vague data model that cannot be used effectively for data analysis.

By standardising the way data is recorded, the process can be altered so that input data is recorded in one single entity and any analysis can be performed from one place. Using this model, the process can be expanded from Internal-Regional benchmarking to Internal-Global benchmarking, meaning that global data can be logged in one place and the same model used for all analysis.

Structure becomes very apparent in Daily Drilling Reporting. Many companies use very different applications, formats and codes to report data. This lack of standarisation causes a number of problems.

‘How can data that is logged with different degrees of accuracy be compared?’

For example, if one solution logs all BHA time under a single entity ‘BHA’ and another logs individual segments for ‘Making up BHA’, ‘Run in hole BHA’ and ‘Pull out of hole BHA’, how can time be sliced in the first solution so that each category can be analysed accurately?

Through actively streamlining all input data to a managed, centralised database, data analysis and performance engineering can be improved, so that future well planning can be improved. This can clearly result in cost reduction and performance improvement.
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

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

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

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

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

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

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

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

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

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

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

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

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.
Keep Calm and Carry on: How Dynamic Modelling and Optimisation has Helped to Maintain Production across the Azeri-Chirag-Guneshli Field
Authors: Calvert Patrick , Pashayev Ahad
Published: June 2014

The Azeri-Chirag-Guneshli (ACG) field is a giant oil development located in the Azerbaijan Sector of the Caspian Sea. The presence of multiple layers and a secondary gas cap has seeded a range of transient phenomena that includes well slugging, cross flow and gas coning.

Dynamic transients seen across the field have contributed to a detrimental impact on production. Well slugging has led to the failure of downhole screens and decreased production. The cross-flow of water between the different reservoir layers is another problem that can increase the water cut by 30% during start up, making it extremely difficult to return a well to production. The gas to oil ratio (GOR) remains very dynamic. When shut in, gas will migrate away from the well bore leaving little free gas to lift the oil. Similarly, the GOR will rise during periods of production as gas is coned into the well. As a result production can be backed out.

Underpinned by extensive dynamic modelling work, BP, together with its Partners and the State Oil Company of the Azerbaijan Republic, has been successful in deploying technology to assist in stabilising and enhancing production across the ACG field.

Well uptime has been improved by optimising the dynamic transients during start-up. Conditioning the well before it is shut in has proved effective at reducing the cross-flow of water that occurs immediately afterwards.

In this paper, the challenge of optimising the dynamic transients across the ACG field will be discussed. Particular emphasis will be given to the solutions developed to address the underlying transient phenomena and how these solutions were deployed at scale and pace across the field.
Development of Hybrid Intelligent System for Virtual Flow Metering in Production Wells
Authors: Bello Oladele , Ade-Jacob Sam , Yuan Kun
Published: June 2014

The demands placed on operators to effectively manage production operations become more complex and challenging with increasing numbers of high recoverable oil and gas fields in frontier environments (remote onshore, deepwater and ultra- deepwater). Predicting flow rates of wells located in these frontier environments accurately is of great importance to enable asset performance monitoring, field surveillance, production accounting, production optimization, reservoir management decision, volumetric input to reservoir simulators and reservoir estimate tracking, though out the field life cycle. Although several studies have focused on real-time well rate estimation using physical multiphase flow meters, theoretical approach (mass, momentum and energy coupled with real-time field data) and artificial neural network models, little attention has been given to continuous flow rate surveillance based on hybrid intelligent systems. Hybrid intelligent systems combine intelligent techniques synergistic architecture in order to provide solution for complex problems. These systems utilize at least two of the three techniques: fuzzy logic, genetic algorithm and artificial neural networks. The goal of their combination is to amplify their strengths and compliment their weaknesses.

This paper presents a novel approach of using hybrid intelligent modeling technique, available time series field data and well configuration information to develop a virtual flow meter for production wells. The simulation results from the hybrid intelligent based virtual flow rate meter are compared to those obtained from real life field data. The validated model is used to predict future performance of existing wells. The effects of various parameters are performed to determine their impacts on the predictive accuracy of the new approach.
Cloud-Based Data Management in Oil and Gas Fields: Advances, Challenges, and Opportunties
Authors: Bello Oladele , Srivastava Deepak , Smith David
Published: June 2014

Use of automation, control and information technology for creating automated workflows and real-time assets management in the petroleum industry has rapidly grown in recent years. This integration of automation and communication produces large amounts of data, especially with optical fiber-based sensor systems and the need for providing real-time controls and optimization based on data-driven predictive analytics. The transition to the intelligent paradigm opens up new opportunities but also poses a number of challenges. Currently, most organizations are struggling to reduce their computing cost through the means of virtualization. This demand of reducing the computing cost has led to the innovation of cloud data management technologies. Cloud computing offers better computing through improved utilization, reduced infrastructure and maintenance costs, and increased flexibility to help meet changing business requirements.

This paper gives a complete overview of cloud data management systems and their usefulness. In addition, it highlights some of the general cloud data management technologies available in the market and describes some of the development trends. It also examines the current challenges and risks facing the cloud data management technology deployments and provides highlights on the business opportunities.
Real-Time Modelling of Drilling Using Nitrogen Enriched Mud: A Case Study
Authors: Cayeux E , Kucs R , Gibson N
Published: June 2014

This case study involves a well drilled in the Erbil area of the Kurdish Region of Iraq, a region characterized by challenging geological conditions for drilling. To achieve the key drilling objectives, the drilling mud was made less dense by the addition of nitrogen into the mud column. In order to get a full understanding of the downhole conditions using this mud, the complete drilling process was modelled in real-time. The model was driven using a real-time WITSML data feed. This transient modeling software calculates downhole pressures, temperatures, torque and drag and cuttings density at all depths in the well bore in real- time, including the depths where there are no physical measurements.

The transient model is continuously updated in real-time to reflect the drilling processes undertaken on the rig (e.g. pipe movement, mud pump activity, thermodynamics). Surface system variables including virtual mud pit levels are also calculated in real-time. The modelled data is then continuously compared to the sparse data points that are being recorded in real-time, allowing both a continuous calibration of the model with the "as drilled" well operation. The calculation of important drilling parameters such as sliding friction, rotational friction, and hydraulic friction is performed in real-time.

The paper will present the key observations upon the matches between the modelled data and the "as drilled" data and summarise the key lessons learned during the well operations and the real-time modelling processes.
Global Mnemonics: The Dual Challenge of Supporting Decisions and Users
Authors: Krupa James , Beach Jaynee , Stiffel Zoltan , Bharath Sharada
Published: June 2014

Historically the role of data aggregation storage systems has been to acquire and manage real-time drilling data in support of the viewing, analyzing, reporting and preservation of the information provided by service companies during drilling activities. When this practice is applied to all wells in a global organization and additionally available to multiple roles within an operating company the challenges multiply. Global Mnemonics (GM) provides a common mnemonic naming convention that overcomes those challenges. The GM concept attempts to provide a level of standardization to the real-time data acquisiton industry in the realm of naming measured data or computed paramenters. The intent is to allow useful information to be deduced from the names based on regularities. Several service companies supply real-time data services to the oil industry with each company displaying the same or similar data with different names.

This paper explores current applications of GM including the value of abstracting data away from acquisition point naming conventions within a real-time infrastructure. Additionally, we examine complex use cases which are improved by the use of GM. For example – some users only require a standardized real-time data management system to support the preservation of decisions. A completely different class of consumer of the information is looking to the real-time infrastructure to aggregate and abstract the data into the "best available" information at any point in time.
Are Machines Better Than Humans in Crisis?
Authors: Wilkins M. J.
Published: June 2014

There have been several well publicized incidents over the past few years in refinery and petrochemical facilities. Incident reports from amongst others, the Chemical Safety Board and papers from ARC and Marsh McLennan Insurance Company show that many of these incidents can be attributed to operational errors. On closer examination of possible actions during a crisis, such as mistakes due to confusion and time taken to understand the value of information being presented, it begs the question - "Are Machines Better than Humans in a Crisis". In fact, could technology provide better safety and business benefits? On November 4th 2010, Quantas Airlines flight 32, an Airbus A380, took off from Singapore to Sydney en route from London Heathrow. At the time, the A380 was the world’s largest and most technically advanced airliner.

Not long after take-off over Indonesia one of the engines exploded. The incident report said that had there not been five experienced pilots on board that day, the plane could have crashed. Thankfully they saved the aircraft which landed back in Singapore. The conclusion was that the balance of advanced technology and human thinking saved the day. In essence, in a stressful situation both humans and machines have a role to play. This presentation will discuss several plant incidents and see how technology could maybe aid the operator in a crisis and help him to focus on the problem.
Intelligent Energy: The Past, the Present, and the Future
Authors: Gilman Helen , Nordtvedt Jan-Erik
Published: June 2014

The first ten years of Intelligent Energy have been interesting and rewarding. The oil and gas industry has seen many successful IE implementations with significant value delivered. We have established improved communication and collaboration, and seen changes enabled by real-time data and new technologies such as predictive analytics.

However, we have not yet delivered the level and scale of transformation that was envisioned at the start. Our industry has moved more slowly at the same time as the "outside" world – our own homes and other industries – has moved much more quickly. As individuals, we consume information and communicate very differently from ten years ago – e.g., through online booking, social media, and the use of map-based solutions. There is an ease of connecting across the world and "things talk" 1 – but not in the oil patch to any large degree.

In this paper, and based on more than ten years of experience working exclusively with IE and with more than a dozen of the key industry actors, we will present an analysis of the IE domain, lessons learned and suggestions for where we should go from here. We will consider the reasons for our current state, attempting to answer why it might be more difficult to transform in the oil and gas industry than elsewhere 2. Why have some of the barriers been much bigger than we expected? The business case is as strong as it was ten years ago, the technology is more robust, and we have more young people in the industry as well as a higher level of acceptance of technology and change in our personal lives. So we should find it easier to make more rapid progress now than in the past. What must change to achieve that progress?
Use of Computational Fluid Dynamics Model for Evaluating Performance of High Pressure High Temperature Wells
Authors: Yuan Kun , Bello Oladele
Published: June 2014

The importance of performance monitoring is growing as the number of high-pressure, high-temperature (HPHT) wells continues to increase in deepwater and ultra-deepwater fields. Accurate prediction of HPHT well performance is essential for enhancing deepwater oil and gas production operations. Applying computational fluid dynamics (CFD) modeling to three- phase gas-oil-water flow in HPHT well conditions is an emerging concept. Multiphase flow in HPHT wells is extremely complex and depends on a multitude of effects including the properties of gas/oil/water, well geometries and configurations, operating pressures and temperatures, and multiphase flow patterns.

In this work, simulations have been performed for three-phase flow in HPHT wells using CFD modeling techniques. The calculated results for phase velocity, pressure and temperature distribution are discussed in detail. The CFD simulation results are compared with measured field data and a finite difference-based mechanistic model. Reasonable agreements are achieved.

Extensive parametric analysis of the three-phase flow system is performed to validate the viability of the CFD model as a predictive tool. The CFD model is demonstratively capable of predicting pressure drop and multiphase flow profiles in the HPHT well throughout various simulated production conditions. The experience gained from the CFD model studies can potentially improve the operations of HPHT wells in terms of increased productivity and enhanced reservoir management. In addition, results demonstrate the capability of the CFD model to predict various multiphase phenomena associated with HPHT well operations.
The Pace of Change: Can We Keep Up? A 35 Year Reflection and Projection on the Future of our Industry
Authors: Roberts Steve
Published: June 2014

35 years ago, when the author joined the industry, the tools of the trade were coloured pencils, ten point dividers, hand drawn maps and contours, with computers just coming onto the horizon. The fax hadn’t been invented, neither had the CD, the laptop computer or multi-terabyte hard drives. The method of communication was mainly via telephone or telex and the internet didn’t exist in the public domain. Computers were accessed quite remotely via a deck of cards, punched by a typing pool of coders, with most jobs running or failing overnight.

The pace of digital change over the last 35 years has been accelerating at a frightening rate. Faxes have come and nearly gone, so has the CD, and the main frame. It looks like the Desktop and laptop computers are in decline too. What will the next 35 years bring? How will we ensure that the oil and gas industry can continue to keep up with this accelerating pace of change?

This paper offers some thoughts on what might be coming next. More importantly, it offers approaches that might help the industry prepare and embrace the ever increasing pace of digital change, allowing the opportunities it provides to be incorporated in an industry where extraction technology changes relatively slowly and major investments last for decades. The paper discusses approaches to managing digital obsolescence and ‘future proofing’ to help anticipate and adapt to new digital technologies. The next generation coming into the industry will expect it to be operated in a digitally enabled way, otherwise they will go and work elsewhere. The paper proposes that there is no choice but to embrace the oncoming wave of digital experience and intelligence.
Managing Safety Overrides across your Upstream Business
Authors: Richardson David , Grange Brett , Hickey Greg
Published: June 2014

Safety trips are a key element of the safe operation of every facility. As a part of the Emergency Shutdown System, safety trips primary function is to shut-down a facility if any parameter is about to breach a safe operating limit. It is also necessary, however, that during abnormal operating situations these safety trips need to be overridden or defeated in order to facilitate operations which are not possible without the overrides in place. Common examples include starting up a facility and overrides configured for disused equipment. These safety overrides are often carried out as and when required for operational reasons and are not then subject to a rigorous permit to work process. When this happens, it is important to avoid unsafe situations which could increase the risk of incidents.

When safety overrides are applied, it is essential that there is a robust process for managing this to prevent unsafe situations arising. This process needs to be easy to implement and operate to ensure that the risk associated with overriding a trip is properly assessed. It must assure that the operating team are aware of the overrides and that this information is effectively transferred between shifts when they change. It must highlight the most important defeated overrides affecting current operations while ensuring that long term overrides are managed effectively.

This paper describes how BP has used the digital oilfield toolset to roll out a new standardised process for managing Safety Overrides to more than 10 assets in diverse geographical locations in the year after a new management process was published. It describes the approach of integrating the capability into the company’s standard logbook for control room operations to speed up the implementation and for ease of operation.
Trusted Operations--Integrating Operational Performance, Safety and Security Assurance
Authors: Jose S.J. , Arango G. , Flichy P. , Featherly J.
Published: June 2014

Industry reports identify human and organizational factors as contributors to operational incidents. These include lack of standardization, enablement, and compliance with operating procedures at the well site or by the broader (local or remote) support personnel. There is consensus among practitioners and regulators about the need to pro-actively reduce our exposure to these factors, both to avoid potential incidents and to reliably contain the impact of realized incidents.

In this paper we argue that we should improve operations assurance by building trust into our operations and we discuss how to combine existing principles, methods and information technology to achieve this in practice. The approach we will describe takes advantage of a framework developed by the HS&E profession--the "bow tie"-- to analyze what contributes to incidents, what barriers could be put in place to prevent them and how to contain them.

The diagram shows an abstraction of the bow tie model. The left side of the diagram (top event prevention) makes explicit the various threats we face during an operation, the precursor that signal the presence of a threat, and the preventive barriers--people, process, technology--that we should activate to avoid the propagation of the threat to become a top event(at the center of the bow tie). The right side of the diagram (top event response) makes explicit the response workflows that may follow an incident, the escalation barriers that we should activate to contain the impact of the incident, and the consequences that we would face if the barriers fail.