bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving Recovery from Thin Oil Rim by Simultaneous Downdip Gas and Updip Water Injection -Samarang Field, Offshore Malaysia
Authors: Bui Thang , Forrest James , Tewari Raj Deo , Henson Richard , Bakar Mohamad Abu 
Published: July 2010

Abstract
The Samarang Field is a complex stacked reservoir system, consisting of multiple sand packages separated by thin but continuous shale layers. The general reservoir structure is a faulted anticline. Most reservoirs have an oil rim with associated gas cap and have some natural water influx.
During the redevelopment study for Samarang field, an extensive IOR/EOR study was performed, focusing on the largest reservoirs of the field. A full-field simulation model for the M reservoirs (over 25% of the field STOOIP) was built and calibrated with the historical production, pressure, and fluid-contact movement data. Using this model and various cross section models, an extensive investigation was conducted to evaluate the benefit of injecting water and hydrocarbon gas to improve oil recovery in these reservoirs. The results indicated that updip water injection into the gas-cap region of the reservoirs is beneficial in improving oil recovery. Simultaneous updip water and downdip gas injection gave the highest oil recovery, compared to other injection schemes. This Gravity Assisted Simultaneous Water and Gas injection scheme (GASWAG) improved the sweep efficiency of the lower section on the inner part of oil rim with the downward movement of water and at the same time, increases the swept efficiency of the upper section on the outer part of oil rim with upward movement of injected gas.
The investigated EOR schemes for M reservoirs could potentially increase the cumulative oil production by 20 MMSTB compared to infill drilling only. This increase represents ∼7% of OIIP. A significant fraction of this oil production can be produced by reactivating existing idle wells during the application of the EOR process. This paper describes the investigation of the gas and water injection to improve oil recovery in the M reservoir layers.
Proactive Maintenance in the Context of Integrated Operations Generation 2
Authors: Sätra Ulf Skytte , Christensen Rebecca , AS Norge , Tanase Adrian , Koppervik Ingvar , Rokke Espen 
Published: July 2010

Abstract
With increased requirements to improve production availability; and to have a continues updated overview of the condition status of subsea production systems (SPS), oil and gas operators have increased focus to reach beyond the current practice of integrating onshore and offshore centers and processes for continuous onshore support. Integrated Operations (IO) generation 2, is a further extension to integrate operation centers of the operator and their vendors with heavily automated processes and digital services in a 24/7 operation.
In order to reach IO generation 2, Proactive Management as foundation for operations with a preemptive maintenance program giving an early insight of coming abnormal issues is essential. Focusing on knowledge and understanding of how the equipment and installed base performs is essential and will be empowered by a management system which takes a holistic approach and an automatic integrity diagnoses functionality. To achieve IO generation 2, work processes which promote collaboration and continuous improvements and visualization of the equipment will be necessary to facilitate a common awareness and maintaining an optimum field operation.
By continuous monitoring of the integrity of the SPS in an IO generation 2 context, the Condition and Performance Management (CPM) system enables preemptive maintenance leading to maximum production at minimum maintenance cost. Increasingly important and supported by the CPM system, is the enforcing of health, safety and environment as stipulated in company goals and in national and international regulations.
The CPM system facilitates enhanced collaboration, and it is configured to support the identified IO generation 2 work processes regarding work interfaces and roles. The agreed work processes for an IO generation 2 set-up make the base foundation of the CPM system, from day-to-day monitoring and collaboration to in-depth analysis and improvement of the installed base performance.
The Influence of Data Quality on WorkFlows and Decision-Making in Well Delivery
Authors: Sawaryn S. J. , Whiteley N. , Deady A. , Borresen A. , Gibson N. 
Published: July 2010

Abstract
The paper discusses how data quality influences workflows and decision-making in drilling and completions and examines the use of semi-automated processes for quality assurance. With poor data, additional steps are required and workflows must be repeated. In even relatively simple situations, controlled tests suggest that small changes or omissions may have a significant influence on the work efficiency or outcome.
In earlier work, the quality of any data stream has been described in terms of identity, presence, measurement frequency, accuracy, continuity, units and associated metadata. For some of these a degree of self checking is possible, applying simple algorithms to the data stream to detect presence and bounds, with alarms to alert the operator if these are transgressed. In other cases, such as the change in drag and torque with depth, the stream must be checked against a trend, called a pseudo-log determined from the physics. These calculations are performed by "smart agents" directly in real time on the WITSML data feed from the rig. The paper describes the early work developing smart agents to address data quality and structure of the associated toolkit that can be used to construct more complex agents from a wider selection of data sources, including system generated ones. The computational resources required are also discussed.
The increase in digital data and skills shortage makes manual assurance of all the data streams neither practical nor cost effective. Since current applications are not tolerant of errors and omissions, a step change in data quality is needed if more automated workflows are to be achieved. Greater assurance of the data at source and an improved understanding of the workflows will help.
The Life Cycle Simulator: From Concept to Commissioning… and Beyond
Authors: McArdle S. , Cameron D. , Meyer K. 
Published: July 2010

Abstract
This paper describes the concepts behind the multi-faceted life-cycle simulator and demonstrates its effectiveness in practice. Dynamic simulators have been successfully implemented in the oil and gas industry for process design and operator training and have become a standard tool in problem solving. As the oil and gas industry has evolved, operational concepts have also necessarily developed. Continuing improvements and employment of new technologies in order to optimize processes have become routine in recent years. The life-cycle simulator is a concept whereby a dynamic simulator integrates the new technologies and intelligently utilizes the updated model as an enabling tool for integrated operations and decision support for the industries new developments. This paper describes a case study where a full life-cycle simulator has been implemented. It also presents an online simulation prototype developed in association with Statoil. It outlines a future vision and steps taken for the simulator as it is incorporated further into integrated operations.
"The Predictive Organisation"— How The Defence Sector Improved Asset Availability and Integrity
Authors: Popham Michael , Keely Daniel 
Published: July 2010

Abstract
The Oil and Gas industry shares many requirements and characteristics with the Defence sector; Operations are conducted in remote, challenging, often hazardous environments with little established infrastructure. Increasingly, remote monitoring is being used to provide more efficient inspection and maintenance of critical equipment. This provides valuable expert resources with effective status monitoring and the ability to function across an organisation, irrespective of location.
Drawing on experience from the Defence sector, this paper discusses how organisations use remote monitoring to deliver predictive, rather than reactive maintenance. It explores the benefits of integrating predictive maintenance with logistics and supply chain management to create a "predictive organisation" which can project and then optimise asset availability and integrity. New approaches for operators to consider are identified which are proven in the Defence sector.
When applied to a fleet of over 240 fast jets, these methods delivered £1.4bn cumulative savings over 6 years whilst meeting availability targets. The annual OPEX budget for the fleet fell from £711m to £328m. The customer was able to demand additional availability as required.
Areas examined include: -
– What drivers underpin remote monitoring solutions in both industries? What are the key challenges? – How can data from a wide range of existing sensors be better used to provide reliable decision support? – How can information from a disparate range of equipment from multiple vendors be integrated into asset availability models? – How are availability levels assured, even across large portfolios? How is increased availability delivered on demand? – How can information derived from remote monitoring be used in a wider collaborative network to deliver increased benefits? – What security challenges do collaborative, multi-organisation remote support centres introduce?
The paper concludes with a summary of lessons identified and an example of how these are being harnessed in Oil and Gas.
Leveraging Web 2.0 Technologies for Innovation Capture
Authors: Watt Andy R. 
Published: July 2010

Abstract
To fully leverage innovation within an organisation, communication channels must work both ways. We need to move away from the old broadcast model to an open dialogue with the target audience, ensuring the message is understood and the call to action is being addressed.
Using open source software, the "Woodside Innovation & Improvement Zone (WIIZ)" is one example of how Web 2.0 technology is being used to create a culture of innovation within the company.
The WIIZ incorporates a number of Web 2.0 concepts: tagging, user participation, voting, design simplicity and two-way communication. Importantly, the software used is not of great importance, a number of different tools could have been used to achieve the same goal. Simplicity, usability, flexibility and cost effectiveness were the primary technology drivers.
The WIIZ is in a "Perpetual Beta" state allowing user-driven enhancements to be made and fully transparent two-way communication applies to the innovation ideas and to the actual site design and functionality.
The site is currently administratively time-consuming, with future enhancements to be made to reduce this overhead. User feedback has been very positive from all levels within the organisation. The project was not started specifically as a Web 2.0 demonstration, but rather has used this technology where appropriate.
Capturing Woodside's innovation and continuous improvement ideas, projects and challenges in a fully transparent, community driven, collaborative way allows us to harness the skills, creativity, experience and passion of our staff more effectively than ever before.
A Guided Workflows Approach to Oil Field Management
Authors: Daum Steve , Getty Robin , Berg Frans van , Clinton Robert 
Published: July 2010

Abstract
It is evident that Digital Oil Field Projects have succeeded in making reservoir, production and operations data more widely available. However, it is also evident that many operators still fail to realise the full value of these data. Too often, oil field professionals are overloaded with information with the direct result that the organisation fails to respond appropriately within the available window of opportunity. The authors believe that in order to realize sustainable improvements in operational efficiencies and in decision-making for production optimisation and reservoir management, the Industry now needs to find ways to ensure that the new streams of information and knowledge that result from Digital Oilfield Projects can flow freely throughout the organization. IT solutions need to do more to screen, classify and rank data, to present the most significant information to the most appropriate person as and when it is needed, to marshal the right analysis tools and configure them optimally for the task at hand, and to route critical actions and results across the organization in the manner most appropriate to the prevailing circumstances.
Much effort has been spent attempting to devolve increased levels of governance to computer systems, including responsibility for the management of workflows, but workflow automation solutions applied to date have tended to be too prescriptive and too inflexible to cope with the dynamic and varying circumstances that humans invariably have to deal with in a real world production situation. This paper describes the piloting of a "guided workflows" solution for Well Test Validation in one of Shell's Operating Companies. This was the first workflow tool built in Shell using PRODML™ connectivity standards.
The solution automates the gathering and pre-analysis of production and well test information, delivering the screened and prioritized results for validation by the appropriate professional, e.g. Production Programmer, before the start of the working day. PDS incorporated human workflow extensions into Shell's production web portal that were used to guide the user through the most appropriate analysis steps and route results to the appropriate team member and other company experts for further analysis and actioning. The solution incorporated overviews of the progress and status of well test validation activities across the company, and provided audit trails to support organizational learning and compliance checking at the granularity of individual tasks and decisions.
The pilot results indicated significant potential for the approach in related areas e.g. production surveillance, optimization and reporting. The extensive use of the PRODML™ standards favourably impacted systems integration project costs and timescales, and increased software reuse.
Machinery Predictive Analytics
Authors: Rawi Zaid 
Published: July 2010

Abstract
BP has deployed early warning systems that monitor heavy machinery, markedly increasing the reliability, operational integrity, and performance of equipment in BP's operations. Early warning allows BP to more effectively schedule maintenance (with costs of planned maintenance typically being a fraction of the costs of emergency repairs) and plan to minimize any production loss during the work through reconfiguration and feed rerouting. The company's initial trial generated value of $2 million during the trial alone, at a cost of only $50,000. Additional trials provided further savings of almost $3 million through detections such as pump seal problems, failing instrumentation, control problems, and turbine fouling. These striking benefits led BP to plan a significant deployment across the company.
In just over a year, BP has made significant implementations at more than half of its facilities in one business segment and online pilots in all other business areas. Millions of dollars have already been saved; BP's goal of significantly reducing unplanned maintenance is well on its way to being realized. Far fewer experts are required to spot developing problems than with traditional data monitoring methods, this can now be done by a skilled technician; so more problems are caught. Quick- win maintenance savings more than paid for the technologies in the first year, and the safety benefits in a potentially hazardous environment are priceless.
BP's aggressive adoption of wired and novel wireless technology to capture more measurements has significantly increased the volume of data available. This wealth of data put BP in an even better position to leverage predictive analytics technology. The technologies’ data-driven approach has many advantages over the traditional trending or first principles models used in the past. It's generally faster to implement, easier to maintain, does not require sophisticated engineering knowledge, and makes use of a wealth of existing but unused data, representing a breakthrough in the area of equipment health.
Remote Optimization Improves Drilling Performance in US Land
Authors: Estep Nathan , Parsons Barry , Middleton Rod , Alvi Ahsan 
Published: July 2010

Abstract
An Operation Support Center (OSC) was established in Colorado, USA to support clients drilling in the Pinedale Anticline in Wyoming as well as other operators throughout the onshore drilling market of the continental US. In the Pinedale Anticline region, operators plan to drill several hundred wells per year. The time to drill and complete a typical well stood at thirty-five days through 2007, and a target was set to reduce this to fifteen days. Achieving this target would result in a savings of approximately 2190 combined rig days a year thus saving the operator over $100mm.
A strategy with 2 main elements was developed: a powered rotary steerable service for vertical drilling and remote optimization via the OSC. This approach allowed for no rotary steerable operations personnel to be based at the wellsite during drilling. The new OSC centric processes and procedures developed increased efficiency and allowed fast deployment across a fleet of rigs. The process included a rig-up crew that moved from rig to rig for BHA pick up or lay down.
The remote operations team then monitored operations and began analysis to optimize performance. Using offset data and mean- specific-energy (MSE) techniques, drilling performance targets were set for each depth interval. OSC based drilling engineers alerted the rig when penetration rates were compromised by adverse drilling dynamics, or when input energy needed to be reduced to preserve bit life and minimize trips. The remote team also generated a daily report for each well that continuously compared penetration rates with expected performance targets and captured best practices. This process was used to communicate across the entire drilling organization.
This method of challenging the target was complimented by the operator's ongoing optimization initiatives and resulted in an increased average ROP of 36% through February 2009. Average time to drill and complete these wells was substantially reduced from the original benchmark; with average drilling and completion time standing at seventeen days. This reduction in days has the potential of saving the operator an average of $900k per well.
Laboratory Evaluation of a Chemical Additive to Increase Production in Steam Assisted Gravity Drainage (SAGD)
Authors: Srivastava P. , Debord J. , Sadetsky V. , Stefan B. , Orr B. 
Published: July 2010

Abstract
This paper describes a laboratory method used to evaluate an additive to enhance bitumen production in a steam assisted gravity drainage (SAGD) operation. This steam additive enhances bitumen production by accelerating the release of bitumen encapsulating the sand grains. The condensed water is then able to carry a higher loading of this released bitumen. A lab-based gravity-driven displacement process was used to simulate the thermal recovery process by passing pressurized steam at 200°C and 2000 kPa over an unconsolidated oilsands core sample. In these experiments, a dosage rate of the additive at 1,000 parts per million (PPM) resulted in a 13.5% increase in bitumen recovery from oilsands compared to the baseline (no additive, pure steam). These lab-based test results establish the effectiveness of this steam additive to enhance bitumen production at a dosage rate as low as 1,000 PPM and thereby precluding any need for recovering the chemical driven by economics.
Integrating Data Mining and Expert Knowledge for an Artificial Lift Advisory System
Authors: Vega E. De , Sandoval G. , Garcia M. , Nunez G. , Al-Kinani A. , Holy R. W. , Escalona H. , Mota M. 
Published: July 2010

Abstract
This paper describes a new workflow to accelerate and improve decisions regarding where and when to apply an artificial lift system in fields with a considerable number of active wells. The workflow deploys a hybrid combination of a user-driven expert system and a data-driven knowledge-capturing system calibrated with historic data. Both systems interact to determine the right point in time to support a particular well with an artificial lift system.
In the case presented, the mature gas field has a large number of operating wells, with predominantly manual operating data entry and a long processing time for newly acquired data. Due to the rapid decline rate in many wells, however, quick decisions are needed to improve productivity and hence the economics of each individual well. During the first two phases of the project, the asset team focused on data collection and workflow automation to speed up well production surveillance operations (e.g., gas rate calculation, estimation of critical velocity, etc.) (Mota, 2007). This paper documents the third phase, which addresses the knowledge-capturing and advisory components of the solution.
Mature fields typically have significant field and asset expertise and a huge amount of historic data. Both information sources—the expert documentation and historic data—can be integrated to investigate past decisions and identify an optimum approach to field interventions. This paper describes the setup and implementation of a hybrid model that combines expert knowledge from asset engineers with the new knowledge discovered through the latest data-mining technology. The resulting system is then implemented in a fully automated workflow that identifies which wells require artificial lift.
The results from a case study in a North Mexico gas field are presented. The reservoir is highly compartmentalized and requires fracturing as a way to increment well productivity. The data-mining approach used in this study is a special visualization technique, the self-organizing map (SOM), and a clustering algorithm (Zangl, 2003). The model was trained with historic production data, well test data, and information about historic well intervention decisions. In addition, expert knowledge from the asset engineers was introduced. The combination of data and expert knowledge enabled fast and reliable identification of the optimal time to install an artificial lift system to increase production while also effectively managing costs.
This system reduces the typical decision time from several days to a matter of hours. The automated workflow runs immediately after the data is acquired and provides a continuous, up-to-date, and ranked list of proposed wells for artificial lift analysis. When new decisions are taken, the model can be updated for future use. The rapid analysis and decision cycle reduces lost production and improves overall field and asset value.