bluuebunny/crossref_metadata_2025 huggingface dataset:
Pace and Scale Deployment of a Real-Time Information System
Authors: Toth D. A. , Dickens J. S. , Overton D.
Published: February 2008

This Paper will describe in detail the process by which a newly developed real time information system was deployed at pace and scale to the Upstream community of a major integrated oil company. The primary focus will be a case study which demonstrates how an application can be successfully deployed across a wide range of cultures in order to bring standardization to the way real time data is accessed and used to generate value.

The ISIS (Integrated Surveillance Information System) program, developed by BP, provides distinctive real time well surveillance functionality. Delivering value from this technology development depends critically on successful deployment at a global scale.

This paper describes the Deployment Model used to guide such a full scale deployment program. Important pieces of background information will be presented first, followed by a review of the model and a description of its use.

Prior to full scale deployment of the final ISIS product, early beta versions were deployed. Learnings from these early deployments provided the basis upon which the final model was built. A review of lessons from these early efforts identifies potential pit falls and defines the elements of the final Deployment Model that are critical to an effective program.

This model has been applied to deploy ISIS to more than 20 fields spread over several regions worldwide, including amongst others, the UKCS, Gulf of Mexico, and West Africa.
Neural-Network based Sensitivity Analysis for Injector-Producer Relationship Identification
Authors: Demiryurek U. , Banaei-Kashani F. , Shahabi C. , Wilkinson Frank
Published: February 2008

Determining injector-producer relationships, i.e., to quantify the inter-well connectivity between injectors and producers in a reservoir, is a complex and non-stationary problem. In this paper, we present a neural-network-based sensitivity analysis approach to address this problem. To the best of our knowledge, sensitivity analysis has never been applied for identification of the injector-producer relationships, yet we show that it is an intuitive while fundamental approach to address this problem. Sensitivity analysis is based on a theory with which the functioning of a closed system is derived by analyzing the derivatives of the output with respect to each input combination. For the injector-producer relationship identification problem, we use sensitivity analysis to determine the injector-producer relationships by varying the injection rates, i.e., the inputs to a trained neural network model of the oilfield, and analyzing the outputs, i.e., the production rates.

With our approach, we first generated a neural network to define the mapping function between each producer and its surrounding injectors based on the historical injection and production data. We employed Back-Propagation-Through-Time (BPTT) learning algorithm to train the three-layer feed-forward neural network using real data collected from 1911 to 2005. Next, we utilized the generated neural network model to apply sensitivity analysis in order to quantify the significance of the injectors on the corresponding producers.

We evaluated our proposed injector-producer relationship identification technique by experiments with real oilfield dataset as well as field trials. Experimental results show that our sensitivity analysis approach is not only an efficient method for identifying injector-producer relationships but also reveals significantly higher correlation accuracy as compared to the correlation typically estimated by the field engineers.
Use of Agent Structures for Event Detection: Identification of Wells Watering Out on Troll
Authors: Halmøy Randi-Helene , Verhelst Frédéric , Díez Marta Dueñas , Halvorsen Martin , Nordtvedt Jan-Erik
Published: February 2008

The industry-wide focus on Intelligent Energy in recent years has lead to more and better data being available, both onshore and offshore. Regrettably, processing tools have not seen a proportional improvement, and therefore engineers experience a data overload. Fairly simple alarms along with manual routine checks using independent, "offline" tools are dominating the daily tasks in production optimisation, and more intelligence and flexibility is thus needed. In this paper we suggest an agent structure allowing online, real-time event detection.

In this work producing oil wells watering out on Troll were used as an example. The detection algorithm was designed to search for significant drops in the wellhead pressure. By detection of such trends at an early stage, a successful correction and restabilisation is more likely.
Next Step of Modeling Technologies: Live Model
Authors: Gladkov A. V. , Kondakov D. E. , Zakirova G. F.
Published: February 2008

Many experts admit a big gap between reservoir simulation and daily management of oil and gas fields. Because only a limited number of specialists can use the results of modeling and models get out of date quickly. The necessity to reduce access and update time has driven to development of a Live Model concept. It will reduce the gap between modeling and practical oil and gas field management.

The targets of Live Model concept are: use models as interactive means of teaching;make models available to everyone, everywhere, anytime;build updating and monitoring tools for models.

In accordance with Live Model concept we developed web-system which enables to perform model monitoring and analysis of reservoir simulation results. Using this tool any specialist by means of a web-browser can do the following: make analysis of dynamics well data;make maps, streamlines visualization;estimate the quality of the models history matching;create and calculate a simple model.

This web-system can act as an integral part of corporate information-engineering portal. Then we have opportunity to use dynamic production data, which is stored in corporate database, and automatically update Live Models. The interaction of web-system for models monitoring and analysis with the corporate database and models accessibility helps the oil and gas companies to use modeling results in the process of decision-making more efficiently. Another crucial factor is the opportunity to employ Live Models as training aids, which significantly reduces learning curve and increases the quality of training.
Leading Edge Techniques to Prepare your Organization and Workforce for Oilfields of the Future
Authors: Chapman Adrian , Kourtis Nikos
Published: February 2008

Remoteness of operations, scarcity of expert resources and a new hierarchy and expectations amongst partners in the E&P ecosystem expose organisations to increased risks and costly failures.

Implementation of Intelligent Oilfield initiatives is at an inflexion point between piloting and the deep business change required to make them part of a new business-as-usual. Many initiatives have struggled to realise the full value anticipated due to a lack of technology adoption.

There is an opportunity to introduce and institutionalise new behaviours and ways of working to help unlock the value of technology investments. Improvements in efficient, effective and safe execution will become the new sources of competitive advantage.

All this requires new organisation structures and workforce change in terms of skills, knowledge and working at a distance.

Innovative solutions are required. As a leading-edge, innovation-driven industry, E&P demands the same approach to organisation and workforce preparation that is applied to its core business. Entrepreneurial, can-do, E&P culture needs to be complemented with an organisation building mindset to implement lasting change.

This paper looks at the ‘future of work’. Based on research in business change, management and technology, it brings the insights needed to address the E&P organisation and workforce challenges for the oilfields of the future.
Real-Time Collaboration—Efficient Problem Solving and Extending Resources
Authors: Hickman A. , Guidry A. , Seaton S.
Published: February 2008

To meet the challenges to the industry of increasing hydrocarbon demand, increasing well complexity, reduced employee experience levels and the large physical distances between operational centers, advances in digital technologies are being increasingly leveraged by both operator initiatives and service company initiatives such as Halliburton's Digital Asset. Terms such as "smart wells" and "real time" have become more commonplace. Data is being generated faster than ever. The ability to interpret this data, model the data and implement optimized solutions in real time is critical to operational success. The demands placed on operating in a cost efficient manner, with greater returns on investment are ever present.

The use of a Knowledge Management collaboration tool, a key component of the Digital Asset, helps to meet these challenges by providing a real time collaborative environment which spans global operations, supports and develops synergies between multiple disciplines and transcends geographical and language barriers. Through its use an intentional shift in focus has taken place from centrally located sources of expertise to virtual ones. Virtual centers of collaboration empower users to collaborate, problem solve and share knowledge on demand. Any user, i.e. employee, can rapidly access the global expertise needed to put well challenges, potential solutions and increasing volumes of data and information in appropriate context. Through access to these extended resources employees can solve problems more efficiently and offer better solutions. Technical experts can cover more ground. Collaboration is facilitated by dedicated personnel who maintain a vital link with local, regional, and global technology leaders.

Examples from Canada, where the use of this approach contributed to an HTHP well being saved, along with an estimated cost of $15 million, from China where urgent advice was delivered to a rig experiencing an underground blowout and from Brazil where global experts collaboratively contributed to solving a wellbore stability problem will demonstrate how real time collaborative solutions are developed and moved from the virtual to real world environment to improve operational service delivery to external clients in the global market place. Lessons learned, best practices and strategies employed to engage users in the use of this collaborative environment are outlined.
Downhole Flow Control for High Rate Water Injection Applications
Authors: Barrilleaux Mark F. , Thomas Boyd A.
Published: February 2008

Smart completions that can remotely control the flow from multiple layers of a reservoir interval were introduced in the mid 1990's. Downhole flow-control (DHFC), as it has become known, has since been installed in hundreds of wells. However, there has been very little use of these valves to control water injection distribution within the layers of a reservoir interval at high rates (>25,000 BWPD) in a continuous proportional operating mode. This paper will review BP's efforts to team with manufacturers to deliver new technologies that can reliably provide this functionality.

By 2010 a significant portion of BP's production will come from complex water flooded reservoirs, in an environment of rising operating costs. The injection wells in these fields need to accept, in some cases, up to 65,000 barrels water per day. The use of DHFC reduces the number of injection wells by using one wellbore to enable conformed injection into multiple intervals. This eliminates the need for injection wells dedicated to a specific layer to achieve injection conformance.

BP has been progressing the development of DHFC systems for water injection wells since 2001 through collaboration with technology developers. Assurance related activities are an important aspect of these technology developments. This paper will describe several assurance activities and the scope of work involved in each, including: Development of a Basis of DesignDevelopment of a Statement of RequirementsFMECA (Failure Mode Effects and Criticality Analysis) at component and system levelsDetailed Design ReviewBase performance qualification testingFull scale HALT (Highly Accelerated Lifetime Testing) of critical featuresStack-up and system integration testsField trialsEstablishment of Global Quality Control Plans for manufacturing

A case study will be presented, describing the application of the above processes to a choking downhole flow control system being deployed by several BP Projects.
BP Deepwater Gulf of Mexico Integrated Field of the Future Case Study
Authors: Castro A. , Fortune S. , Maio J. , Warburton S. , Sauermann R.
Published: February 2008

This paper will take us through the journey of integrating BP's Gulf of Mexico Information Management and FIELD OF THE FUTURE (Reddick, 2006; Reddick, et al, 2008) Strategies to deliver a powerful, combined technological solution. Only when both of these pieces are working together, can you truly start to use IT and automation technologies to optimize the business.

The paper will start with the information management story four years ago when we had four producing assets and four major projects behaving as individual businesses. It will then describe the creation of a strategy that focused on getting the information foundation right first.

The paper will then describe the integration of the foundation data sources into a web portal and organized in the way that people do their job. The paper will contain examples of real time data systems that give our asset support teams and vendors access to performance data on their machines. The paper will also discuss new processes that allow the advanced collaborative environments to take advantage of the new kit.

Finally, the paper will discuss the optimization efforts that are possible after a robust information foundation layer exists and is tightly integrated in the way that people do their work.

The lessons learned in the Gulf of Mexico over the last four years are: Need merging of FIELD OF THE FUTURE and Information Management strategiesNeed new processes developed and support models defined for new toolsNeed to use common information management architecture across entire business

Benefits that we have seen for our combined strategy are that: Faster, more coordinated responses to severe weather eventsReduced personnel on board thereby lowering HSE risk and lowering our operating expenseIncreased worker productivity due to much quicker access to and visual presentation of the dataMore rapid development of business tools
Realizing Value from Real Time Well Monitoring in BP's Field of the Future Program
Authors: Feineman David R. , Newman Mark , Campbell Michael G.
Published: February 2008

BP's FIELD OF THE FUTURE program makes use of in-well sensors, automation, and computer networks to move real time operations data to remote sites for analysis, enabling informed and faster intervention decisions. Realizing the value from such technology investments is not automatic, and requires an integrated approach to technology insertion that holistically addresses diverse issues of technology installation, stakeholder engagement and alignment, business process change, roles and skills as a managed project. We refer to this type of integrated approach applied to digital oil field developments as business transformation, which was described in outline in SPE 99779. This paper describes a recent case study from a Deepwater Gulf of Mexico field where there has been rapid and quantifiable business impact from the use of real time well data in the performing dynamic surveillance and allocating production processes.

BP's proprietary Integrated Surveillance Information System (ISIS) was put into global deployment starting in 2006. The system delivers well performance data to field and office sites in near real time. To realize the value from the ISIS tool, asset teams must be informed and aligned about the possible impacts on roles, skills, and business processes before, during, and after technology deployment. The case study starts with an examination of some of the historical factors that have limited rapid, global adoption of new technologies in BP. It describes the unique aspects of the overall business transformation project design and timeline, and some of the key work products required to establish both the case for change and business case. Finally, it presents some of the dimensions of business value that can be associated with the use of intelligent well information.

Looking forward, the paper describes some of the remaining challenges and opportunities dealing with scale up, acceleration, and centralized business process governance.
Modelling and Optimisation in BP Exploration and Production; Case Studies and Learnings
Authors: Stenhouse B.
Published: February 2008

This paper discusses the role of modelling and optimisation in BP's FIELD OF THE FUTURE Short-Loop optimisation programme. The focus is on the short-term optimisation of oil and gas production subject to the constraints imposed by hydrocarbon reservoir management and the associated protection of reserves. Reservoir modelling itself is not discussed; rather it is assumed that an effective reservoir depletion strategy has been defined and can be used to set boundaries for the short term optimisation of production.

The primary objective of the paper is to support the case for the widespread use of mechanistic modelling in support of operational decision making. Past experience in BP, with modelling and optimisation technologies, has shown that there is a huge potential for increased production from the use of models in support of daily operations, once the quality of these models has been assured.

The paper reviews the current business drivers for optimisation in BP and summarises some of the historical experience with model based optimisation in BP's Exploration and Production segment. The paper concludes by summarizing some of the key learnings that the FIELD OF THE FUTURE programme is taking forward as the basis for the design and deployment of a new optimisation capability within BP. called Model Based Operational Support (MBOS).
Examples of Right-Time Decisions From High Frequency Data
Authors: Shyeh J. Jane , Hehmeyer Owen J. , Gibbeson John M. , Mullins J. Jason , Trujillo Dickson
Published: February 2008

Asset performance can often be improved through continuous monitoring and/or through better utilization of information extracted from the high frequency data that are becoming more readily available in today's digital world. ExxonMobil has a long history of applying advanced technologies in asset management. Today, we continue to use new hardware, integrated software, and improved data infrastructure to enhance asset management workflows. ExxonMobil is taking an enterprise-wide approach (Reece et. al. 2008) to implementing digital technology in asset management.

This paper presents four examples where ExxonMobil has taken advantage of high frequency data for timely asset management decisions. These four examples represent implementation at four different operational scales: for reservoir management, for well management, for facility management, and for plant management. The four examples are: (1) For a West African reservoir: Permanent downhole pressure gauge data have added value in reservoir modeling that in turn provided a method for calculating reservoir rates; (2) For South Texas gas wells: Real-time data access and charting capabilities were implemented and advanced data analysis explored to identify well events and manage well work-over activities aided by artificial intelligence; (3) For Norwegian oil fields: An integrated facility model was developed and tuned for surveillance and operation of a network of wells and production facilities that are shared by multiple fields; (4) For an Australian production plant complex: Production from offshore platforms, a gas plant, a crude stabilization plant, a fractionation plant, and a tank farm was optimized with high frequency data and automatic process control.
A Standard Solution for Upstream Oil and Gas Surveillance
Authors: Crawford Mark L. , Hoefner Mark L. , Oakes Jack E.
Published: February 2008

ExxonMobil* is developing and implementing new systems and work processes that will increase the efficiency and effectiveness of well, reservoir, and facilities surveillance across their operated assets worldwide. For opportunities with a significant information technology component, often referred to as "digital oilfield" initiatives, ExxonMobil applies a broadly consistent, systematic approach to the identification of technologies or suites of related technologies which will add quantifiable value given our asset mix and organizational structure. To derive the greatest impact and efficiency, the new technologies are then implemented within established standard computing systems which allow scalable global solutions.

Improved surveillance, whether it applies to well productivity, well integrity, reservoir performance, or surface facilities, has been identified as an area with a significant opportunity for volumes uplift and for improved work efficiency. ExxonMobil is developing a number of new surveillance tools, processes, and systems, some of which will utilize proprietary in-house technology with others developed jointly with vendors. This paper will focus on one such initiative, termed "Production Surveillance and Optimization" (PS&O). The PS&O initiative is a combination of enhanced IT capabilities and ExxonMobil-standard surveillance processes and best practices that will be employed worldwide starting in 2008. Key features of the PS&O tool are that it connects to disparate datastores such as corporate data repositories and data historians, allows automated alerting when production conditions deviate versus expectations, allows automation of follow-up diagnosis and analysis, and provides access to proprietary best practices relevant to the task at hand.
GeDIg: Petrobras Corporate Program for Digital Integrated Field Management
Authors: Moisés G. V. , Rolim T. A. , Formigli J. M.
Published: February 2008

Foreseeing near future opportunities for oil and gas fields, Petrobras created a corporate program dedicated to study, develop, and implement Digital Integrated Field Management (GeDIg) among its production assets.

Over the last three years, Petrobras has been developing a pilot strategy based on multiple scenarios to evaluate the technology level of digital oilfields. Six assets were chosen, taking into account the diversity of production processes (heavy oil, offshore, onshore, brown, and green fields) found all over the Brazilian fields. Two different approaches were implemented: in-house development and partnership with integrated companies.

Petrobras program is supported by three fundamental elements: people, process, and technology. Humanware, workflow processes, and change management are the key factors for new technologies implementation such as collaboration centers, intelligent completion, and fast loop artificial lift optimization.

After the pilots first year of operation, lessons learned will be gathered to guide the expansion of the digital oilfield concept for other Petrobras assets. The objective of this work is to describe the methodology applied in the six pilots and how Petrobras is going to improve its digital way of work and add value to its assets with Digital Integrated Oil and Gas Field Management.
The Fazenda Alegre Journey into Intelligent Energy — Lessons Learned from a Successful Holistic Approach of People, Process and Technology Aligned to Business Strategy and Results
Authors: Vinturini W. L. , De Castro G. B. , Crespo J. H. S. , Albernaz J. R. , Lopes M. A. S. , Valadares A.
Published: February 2008

Petrobras created a corporate program designed to guide and implement Digital Integrated Field Management among its production assets, named GeDIg. Fazenda Alegre field (FAL) was chosen as the Espírito Santo Business Unit pilot. The FAL field is an onshore brownfield which produces naphthenic heavy oil from 59 wells with cycled steam injection as improved oil recovery (IOR) method. Management and technical teams from FAL are geographically dispersed among three different locations: its logistics base in São Mateus city, its business unit headquarters in Vitoria city and FAL facilities located 210 km North of Vitoria and 40 km South of São Mateus.

GeDIg-FAL pilot scope comprises the deployment of an integrated portal giving users access to several new technical solutions and collaboration capabilities. Business process revisions and change management initiatives to accelerate new work process implementation were all considered. Moreover, the FAL pilot includes the design, construction and commissioning of three collaborative environments for real time production monitoring and process analysis.

This paper's objective is to describe the GeDIg-FAL Pilot Case Study, highlighting the innovations brought up and the means used to deliver intelligent energy capabilities into FAL operation, while sharing results and lessons learned from each project phase, starting with assessment all the way through operation, including a business case study development.
Group Mental Models in Collaborative Work Environments
Authors: Guldemond Ewoud , Ten Have Klaas
Published: February 2008

Individuals in Collaborative Work Environments (CWE's) have interpretations and norms towards the technology they use. We call these interpretations and norms ‘mental models’. These mental models form the perceptions from which other models are interpreted, for example decision-making models. Mental models can be divided into taskwork and teamwork mental models.

This paper will focus on the group level of the (shared) mental models. Not only the use of technology is influenced by the mental models, also the success of learning will, to a large extent, depend on these mental models. So far, the emphasis has been on the development and implementation of Smart Oil Fields Technology as such. Little attention has been paid to the importance of the influence of mental models during the implementation of the new technology. This paper aims to shed some light on the subjective aspect of the implementation of Smart Oil Fields Technology within the CWE's, by providing a model of major factors of influence and outcomes of Group Mental Models in Collaborative Work Environments.
Integrated Operations: How Effective is the Current Relationship between Operating Companies and Suppliers?
Authors: Sund Knut A. , Bratvold Reidar B.
Published: February 2008

Over the past few years there has been an unprecedented wave of capital spending in the exploration and production industry. Still, the expectations for improved capital efficiency from Integrated Operations and its promise of "faster and better decisions" have not materialized. Industry headlines are filled with notable examples of multi-year, multi-billion-dollar overruns. Indications show that leaders of oil and gas companies may be less satisfied with their overall performance than at any time in the industry's history.

In this paper, the main focus is on inter-organizational relationships between operators and suppliers in the context of Integrated Operations. We have surveyed one large operator, three large suppliers, and some small suppliers operating on the Norwegian Continental Shelf (NCS). The survey included a broad range of technical professionals at different management and business levels and included questions related to the collaborative relationship between operators and suppliers. The paper presents and discusses some of the results from the survey. We will discuss the disconnection between operators and suppliers related to contractual/ incentive based contracts. Further, end results with use of incentive based contracts will be illustrated and possible improvements will be discussed.

Improving collaboration between operators and suppliers offers perhaps the greatest challenge and, we believe, the greatest potential in achieving the much anticipated value creation from Integrated Operations. This paper contributes to this by identifying the key disconnects between operating companies and suppliers.
Production Optimization: System Identification and Uncertainty Estimation
Authors: Elgsaeter Steinar M. , Slupphaug Olav , Johansen Tor Arne
Published: February 2008

Real-time optimization of oil and gas production requires a production model, which must be fitted to data for accuracy. A certain amount of uncertainty must typically be expected in production models fitted to data due to the limited information content in data. It is usually not acceptable to introduce additional excitation at will to reduce this uncertainty due to the costs and risks involved.

The contribution of this paper is twofold. Firstly, this paper discusses estimation of uncertainty in production optimization resulting from fitting models to production data with low information content, a concept that has previously mainly been applied in reservoir management. Secondly, this paper illustrates how system identification can be used to find production models which can be solved with little computational effort and which are designed to be easily fitted to production data.

The method is demonstrated on a synthetic example before being applied to a case study of a North Sea oil and gas field. In offshore oil and gas production, the suggested method is expected to have applications in the development of structured approaches to uncertainty handling, for instance excitation planning and real-time optimization under uncertainty.
GeDIg Carapeba—A Journey From Integrated Intelligent Field Operation to Asset Value Chain Optimization
Authors: Lima C. B. C. , Henz C. F. , Lhote J. P. , Kumar A.
Published: February 2008

GeDIg is a Petrobras corporate initiative to implement Intelligent Energy technologies to achieve Integrated Digital Oilfield Management with the sole objective to maximize the life of mature and new producing oilfields. Carapeba is an offshore brownfield located in the Northeast area of the Campos Basin, comprising of 3 fixed platforms with 41 wells producing from three zones (all dry completion and equipped with Electric Submersible Pumps) and 4 water injection wells.

The key business driver is to increase the recovery factor by improving reservoir sweep efficiency with installation of ten intelligent completion systems, achieve production optimization and augment operational efficiency by upgrading field automation and integrated process optimization.

GeDIg Carapeba encompasses end-to-end seamless integration, spanning across various functional groups of the asset, integrating operational processes (fast, medium & long loops). It provides a toolset that gives the ability to make better informed decisions by multi-disciplinary teams in a specially designed collaborative environment to plan, monitor, control & optimize operational processes, making asset teams more agile. The solution is delivered through a portal platform which integrates information from Production Operations, Geotechnical and Financial systems, providing an information hub for the entire asset operations, shielding complexity of underlying sub-systems and empowering end-users with right information in-time. This is supported by field automation, smart simulation and optimization tools which integrate the well-bore, surface facility networks, reservoir, process and economic models. This level of integration provides more transparency to understand engineering and economic impact of various field development decisions.

This paper describes experiences and challenges of the GeDIg Carapeba project from conception thru implementation, integrating people and processes with the right balance of technology across field operations and how it has resulted in enhanced operational efficiency and recovery factor, coupled with a substantial increase in production.
Intelligent Strategies In LNG
Authors: Rangnow Darrell , Govia Delfina
Published: February 2008

The vision for Intelligent Strategies in the oil industry is defined by the optimal integration of business processes and advanced technologies, supported by organizational alignment, to deliver a new standard for decision making. The greatest challenge we have all discovered is in effectively delivering the required change management, and therefore, greenfield opportunities deliver the easiest and the most rapid successes. The segment of the hydrocarbon supply chain where greenfield sites are prominent is in LNG.

Many aspects of LNG business processes and operations are unique. The paper will discuss the uniqueness of business processes stemming from LNG commercial models and the resulting requirement for tight interaction between Supply Chain and Operations, which is executed through the deployment of real-time data and information technologies.

Additionally, the paper will explore how intelligent strategies are especially significant in this industry due to the ownership structures. LNG is a highly fragmented industry, with different players in each segment of the value chain. The fragmentation is further complicated by different company stakes in assets as owners, operators, and suppliers and off takers of the LNG terminal assets. Intelligent strategies deliver the required world-class communication and collaboration capabilities.
Learnings from the Deployment of Advanced Collaborative Environments at Scale in BP North Sea Operations
Authors: Goodwin S. , Critchley C. , Shaw P. , Da Costa N.
Published: February 2008

This paper reports on key learnings acquired over the last two years from of the implementation of five Advanced Collaborative Environments (ACE) projects, as part of BP's global FIELD OF THE FUTURE programme in the North Sea. ACE transforms the way onshore and offshore staff interact and collaborate, with the objective of improving operating uptime and plant efficiency.

The paper will describe: How the behaviours of onshore and offshore teams have changed, and the subsequent impact on business performance.The benefits realised during the early months of ACE operation, including both tangible and non-tangible value recognised.The impact of ACE projects on both the onshore and offshore population.Plans to continue the ACE journey by deploying and exploiting new and existing technologies to: ∘Further improve the efficiency of the offshore/onshore interface.∘Change the working relationship between BP and some of its third party suppliers.∘Deliver global expertise more effectively to the point of need.
Production Surveillance and Optimisation for Multizone Smart Wells with Data Driven Models
Authors: Goh K. C. , Dale-Pine B. , Yong I. , Van Overschee P. , Lauwerys C.
Published: February 2008

The Champion West field was discovered in 1975 offshore Brunei, but its oil reserves in a complex web of thin reservoirs were initially deemed too expensive to develop. Field development was slow due to reservoir complexity and technology limitations. The current phase of development of the Champion West reservoirs uses long horizontal snake wells, which create multiple drainage points in sands, effectively achieving a similar drainage pattern of several conventional wells. The snake wells intersect up to 4 kilometers of reservoir intervals with total depth of up to 8 kilometers, and are divided into several zones with external casing packers or swellable packers. Each zone is then equipped with an inflow control valve and pressure and temperature sensors to allow monitoring and optimization of the recovery process from that zone. Historically, for long horizontal wells, the effective control of production profile and effective tracking of production from individual zones have been problematic. Poor tracking of production will adversely impact overall management and ultimate recovery from a reservoir. One solution is to fully utilize surface and downhole pressure data and multirate well tests to generate data driven models to determine zonal inflow, zonal interactions, and flow across inflow control valves, and to compute ICV settings for optimum reservoir management.

FieldWare Production Universe (FW PU) is a software application developed by Shell International Exploration & Production and Shell Global Solutions International, with significant involvement and support from Brunei Shell Petroleum Company Sendirian Berhad (BSP) for robust data driven modelling in an production operations setting that provides continuous real time estimates of well-by-well production. Applied to the Champion West multizonal wells, the FieldWare PU models, built using surface and downhole test data and an understanding of well performance provides regularly validated estimates of zonal production rates using real time surface and downhole data. Using this tool, inflow control valve settings are suggested to the user in order to optimize production on a daily basis through the use of mathematical optimization routines taking into account all available data. The system also provides early warning to the field management team of any wells deviating from well reservoir management guidelines. The intent of this technology is to enable more transparent, sustainable and systematic management of smart well production systems through the use of real time data to improve the understanding of reservoir behaviour and to allow early intervention to optimize production and ultimate recovery.
Fiber Optics Sensing Systems for Subsea Applications–Sensing Capabilities, Applications, and the Challenges Being Faced in Order to Provide Reliable Transmission of Data for OnLine Reservoir Management
Authors: Drakeley B. K. , Omdal Svein
Published: February 2008

The industry has seen the gradual implementation of step-change optical sensing technology as one way to address the issue of reliable permanent downhole gauges. This paper will include case histories that indicate an ever-broadening acceptance of this technology with application scenarios ranging from simple single-gauge installations to multi-zone intelligent wells with integrated sensing and remotely activated in-well zonal flow control - true Smart or Intelligent Wells, with now over 600 installations worldwide.

Present measurement capabilities include Pressure and Temperature, Distributed Temperature Sensing (DTS), Single- and Multiphase Flowmeters, Seismic Accelerometers and hydrophones, and the paper will also detail additional downhole and subsea optical sensing systems currently under development.

As subsea continues to play a vital role in our industry, it has been recognized that there are a number of challenges to be addressed before optical sensing systems will be widely adopted subsea.

These challenges are being addressed in part by a new thirty member company industry group, SEAFOM (Subsea FiberOptic Monitoring) – this group's activities will be described.

In addition, a number of operating and service companies have on-going related initiatives. For example, StatoilHydro has an Integrated Operations (IO) initiative aimed at significantly increasing overall recovery rates of subsea production assets. As part of that initiative, StatoilHydro and Weatherford signed a three year Technical Development Cooperation Agreement in June 2006. This project will make real-time reservoir monitoring and downhole data available everywhere on a StatoilHydro network infrastructure.

This paper includes the rationale for selecting fiber optics as the baseline technology and also describes the technical solution for an open, high speed communication infrastructure. The project is also developing interfaces towards existing subsea systems with only electrical, low bandwidth communication systems. Solutions are being developed for both Green and retrofit Brown field applications.
Spotting a False Alarm—Integrating Experience and Real-Time Analysis With Artificial Intelligence
Authors: Nybø Roar , Bjørkevoll Knut S. , Rommetveit Rolv
Published: February 2008

Time series data from past drilling operations are an under-utilized resource in oil-companies. Time series record processes in the well, effects of machinery on the rig, and presents an opportunity to improve upon the models underlying drilling simulations as well as alarm systems.

We present a proof-of-concept for reducing false kick alarms during drilling by combining a physical model with techniques from artificial intelligence. We show that artificial intelligence can be used to learn from the experience implicit in time series data. It then corrects for limitations in the physical model, which results in a more accurate prediction of the mud flow out of the well, and subsequently fewer false kick alarms.
The Use of Neuro-Fuzzy Proxy in Well Placement Optimization
Authors: Zarei Faraj , Daliri Ayoub , Alizadeh Naser
Published: February 2008

Optimal well placement is crucial step in oil filed development but it is a very sophisticated process on account of different engineering and geological variables affect reservoir performance and they are often nonlinearly correlated. This study presents an approach where a hybrid optimization technique based on genetic algorithm (GA) and a Neuro-Fuzzy system as proxy was created and used to determine the optimal well locations regarding net present value (NPV) maximization as the objective. Neuro-Fuzzy system was used as proxy to decrease the numbers of costly and time consuming-simulations. Such a system has supplanted a conventional technology in some scientific applications and engineering systems, especially in modeling nonlinear systems. Neuro-Fuzzy modeling is a flexible framework, in which different paradigms can be combined, providing, on the one hand, a transparent interface with the designer and, on the other hand, a tool for accurate nonlinear modeling. The rule-based character of Neuro-Fuzzy models allows for the analysis and interpretation of the result.

Within Hybrid Genetic Algorithm (HGA), a database of the completed simulations is made. This database is used to construct of Neuro-Fuzzy network. Then this network is used to estimate the fitness function at points that no simulations have not been done. This proxy is also able to get better during the optimization each time a new point is verified and visited points database is updated.

A synthetic reservoir was tested and comparisons made among HGA, simple GA and non-proxy using approaches. Results showed that Neuro-Fuzzy system is very reliable proxy to estimate fitness function so the HGA will have a good chance to obtain the optimal place for the well in minimum possible duration.
Production Engineering Experience With the First I-Field Implementation in Saudi Aramco at Haradh-III: Transforming Vision to Reality
Authors: Al-Arnaout Ibrahim H. , Al-Driweesh Saad M. , Al-Zahrani Rashad M.
Published: February 2008

Over the recent years, the concept of the Intelligent Fields (I-Field) has continually been gaining an increasing attention by virtually all oil fields operators. This comes as a natural outcome of the increasing complexity of newly developed wells that provide better solutions to arising reservoir and production engineering challenges. Intelligent Fields are justified by their ability to provide solutions to minimize well intervention, optimize field performance and improve the decision-making and work efficiency. Although, uncertainty still exists as to what actual values can be realized.

Saudi Aramco has recently implemented the I-Fields concept at Haradh-III, a newly developed field within the giant Ghawar field. In the planning stage, the simulation studies have shown the performance superiority of multilateral wells equipped with smart completion. So the field was developed mainly with multilateral smart wells with permanent downhole monitoring systems and surface multiphase flow meters on each well. These systems were perfectly supplemented by a Supervisory Control And Data Acquisition (SCADA) system making the field the first of its kind within Saudi Aramco with the ability to remotely monitor the real-time data and control both the surface and downhole valves.

This paper details the Saudi Aramco Production Engineering experience with the realized I-Field benefits both operationally and economically citing several field case studies. The I-Field has revolutionized the routine production engineering functions like well integrity and production allocations. It has created a new synergy and flow between various sectors in the Company enabling real-time assessments of operations as they are being undertaken. Also, the I-Field enabled taking the advantage of wells’ nonproductive time for regular maintenance to conduct important surveys on these wells.

The experience described in this paper represents the transition between the visionary phase to the implementation phase of the I-Field concept. It will help appreciate how convergence of employed technologies is manifested in real field situations.
From Data Monitoring to Performance Monitoring
Authors: Stundner Michael , Nunez Gustavo , Nielsen Frank Møller
Published: February 2008

The availability of accurate performance information throughout the production system is fundamental to optimization of the economic potential of the reservoir. Without this understanding, a company's field development and operational decisions may not permit the maximization of economic value and may undermine the accuracy of the reserves estimates.

Present digital oilfield technology gives the production engineers all the data needed to monitor process parameters and fluid production, under the assumption that deviation from any target would be detected with just monitoring the data collected. Then, any "good" decision for improvement or optimization would be taken based on this data. However, the answer to questions such as "how good is the decision?" or "how does the production engineer know that it is operating at the best performance possible?" is not easy, because a reference for comparison is needed.

Performance efficiency can be defined as the ratio between theoretical result from a model or test results and the actual results achieved. A Production Performance System (PPS) based on workflows can be implemented to calculate key performance indicators comparing the actual performance versus targets, models, or base case and identifying performance deviation on time in order to avoid undesired lost production. PPS includes an events management and knowledge base system that allows not only detecting events but also identifying possible reasons for them based on historic data and artificial intelligent models.

Our objective is to provide recommendations on how a PPS should be implemented to improve well productivity as well as standardize process and data management. To fulfill this objective, the paper will address the following topics: brief introduction of previous work, processes and workflows descriptions, technology available, real-time environment, automated workflow application recommendations, and potential benefits.