bluuebunny/crossref_metadata_2025_split huggingface dataset:
Production Optimization: System Identification and Uncertainty Estimation
Authors: Elgsaeter Steinar M. , Slupphaug Olav , Johansen Tor Arne 
Published: February 2008

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.
Breaking the Barriers—The Integrated Asset Model
Authors: Tesaker Øystein , Øverland Alf Midtbø , Arnesen Dag , Zangl Georg , Al-Kinani Andreas , Torrens Richard , Bailey William , Couët Benoît , Pecher Radek , Rodriquez Nelson 
Published: February 2008

Abstract
The objective of this paper is to highlight the necessary steps for the successful use of integrated asset modeling. It presents the full workflow for optimzing production and injection cycle times with the help of a simplified reservoir model (SRM) through the set up of an integrated asset model (IAM) to validate the SRM results and control the actual production performance.
A discusson of the theory of the IAM as well as the steps to set up a SRM and IAM are presented in this paper. The steps are described in context of an actual field operation. A WAG cycle optimization workflow for the Snorre field has been created to demonstrate the advantages of using the SRM and IAM technology. The optimization process is performed using a SRM able to run a simulation run in a matter of minutes and hence being suitable for sensitivity analysis and optimization. The optimized WAG injection and production cycle is then carried forward to an IAM in order to accurately determine the well performance and the reservoir production. The IAM couples the modeling results from reservoir and well model with the surface facility network and process plant model. The coupling and integration allows investigating the impact of changes in one model to all the other models and hence also handles the proper propagation of constraints throughout the system.
Valuation Models for Intelligent Strategies
Authors: Govia Delfina , Carpenter Kevin 
Published: February 2008

Abstract
There is intrinsic value in the application of intelligent strategies. Pockets of "value" have been calculated in isolated situations. Yet the resistance to change has created a strong barrier to execution. Robust models for valuing intelligent strategies do not appear to be prominent. At the Digital Energy Conference in Houston in April, several presenters from large oil companies publicly admitted to not having a process in place to quantify the results of their efforts. This paper seeks to provide suggested models to pave the path forward.
This paper will especially highlight the critical success factors for effective valuation: ➢Valuation that supports integrated decision management➢Valuation that takes into account uncertainties➢Valuation that clearly models risk vs. reward➢Valuation that speaks to the organizations multiple levels of stakeholders (Executive/ Investor, Mid-level management, Field Operators)
Real Time Optimisation Approach for 15,000 ESP Wells
Authors: Zdolnik Sergey , Pashali Alexander , Markelov Dmitry , Voklov Maksim 
Published: February 2008

Abstract
In Rosneft Oil Company 90% of production is provided by 15 000 ESP wells. That is why monitoring and efficiency increasing of those wells are of current importance. Up to recent time, analysis and optimisation were done regularly on monthly basis, while real time monitoring systems were oriented mainly to keep books of oil production.
In our opinion implementation of real time monitoring and control systems and reducing analysis and optimisation cycle to days may increase production by the means of rate increase and downtime reduction.
Research in this area started in Rosneft in late 2006 in the framework of New Technologies System. Currently there are solutions for data gathering and proceeding, worked out information use in business processes, has practice in production increase and well downtime reduction, started company-wide application of system.
Paper includes cases of well regime analysis done with the described system.
An important question of quantative evaluation of system applicability for certain conditions was examined. This paper may help to take decision on application of this type of systems for other companies.