Temizel, Cenk (Aera Energy) | Canbaz, Celal Hakan (Ege University) | Palabiyik, Yildiray (Istanbul Technical University) | Putra, Dike (Rafflesia Energy) | Asena, Ahmet (Turkish Petroleum Corp.) | Ranjith, Rahul (Far Technologies) | Jongkittinarukorn, Kittiphong (Chulalongkorn University)
Smart field technologies offer outstanding capabilities that increase the efficiency of the oil and gas fields by means of saving time and energy as far as the technologies employed and workforce concerned given that the technology applied is economic for the field of concern. Despite significant acceptance of smart field concept in the industry, there is still ambiguity not only on the incremental benefits but also the criteria and conditions of applicability technical and economic-wise. This study outlines the past, present and the dynamics of the smart oilfield concept, the techniques and methods it bears and employs, technical challenges in the application while addressing the concerns of the oil and gas industry professionals on the use of such technologies in a comprehensive way.
History of smart/intelligent oilfield development, types of technologies used currently in it and those imbibed from other industries are comprehensively reviewed in this paper. In addition, this review takes into account the robustness, applicability and incremental benefits these technologie bring to different types of oilfields under current economic conditions. Real field applications are illustrated with applications in different parts of the world with challenges, advantages and drawbacks discussed and summarized that lead to conclusions on the criteria of application of smart field technologies in an individual field.
Intelligent or Smart field concept has proven itself as a promising area and found vast amount of application in oil and gas fields throughout the world. The key in smart oilfield applications is the suitability of an individual case for such technology in terms of technical and economic aspects. This study outlines the key criteria in the success of smart oilfield applications in a given field that will serve for the future decisions as a comprehensive and collective review of all the aspects of the employed techniques and their usability in specific cases.
Even though there are publications on certain examples of smart oilfield technologies, a comprehensive review that not only outlines all the key elements in one study but also deducts lessons from the real field applications that will shed light on the utilization of the methods in the future applications has been missing, this study will fill this gap.
The digital oilfield technology is typically associated with high level of field automation and instrumentation, as well as advanced petroleum engineering modelling.
This paper discusses the application of digital oilfield to large brown fields based on real, but anonymous cases, where the level of instrumentation is low, production models might not be available, and the local expertise might be limited.
In such situation, the principles of digital oilfield need to be adapted. This paper presents a staged implementation methodology, where the benefits and costs can be evaluated at every step of the project, allowing to build a system with the right amount of functionality and complexity.
The first step focuses on improving data quality, even if the data is captured manually, through automated quality checks and raising awareness during the data capture process. The second step focuses on automating routine tasks, such as reporting, leading to efficiency improvement, but also increased accuracy and traceability of the reported figures. The third step focuses on developing a production monitoring platform, allowing to perform exception-based surveillance, particularly important for large fields, as well as providing a single point of access for different disciplines, hence acting as a collaborative environment. At last, the model-based more complex workflows are discussed, such as virtual metering, production optimization and short-term production forecasting.
The main conclusion of this paper is that the Digital Oilfield can deliver value for brown fields, even if they are close to their life end. The relatively low cost of these solutions, and the immediate benefits they can provide makes it meaningful, even in a short-term perspective. A staged implementation lowers both the project risks and the required initial investment, while easing the adoption process by the users.
The main differences with application to green fields is an increased focus on data quality improvement, and a lower focus on models and complex engineering workflows. The surveillance platform should also focus more intensively on exception based surveillance, allowing to pre-process large amounts of data, rather than providing extremely fine detail.
Gelinsky, Stephan (Shell International E&P) | Kho, Sze-Fong (Shell International E&P) | Espejo, Irene (Shell International E&P) | Keym, Matthias (Shell Malaysia) | Näth, Jochen (BSP) | Lehner, Beni (BSP) | Setiana, Agus (BSP) | Esquito, Bench (SDB) | Jäger, Günther (SDB)
Prospects below or near shallower producing fields can be economically attractive yet also risky since reservoir presence may be uncertain, reservoir quality can be poor, and high overpressure and temperature can make drilling and logging deeper prospects difficult. Systematic integration of relevant subsurface data from thin section to basin scale allows to seismically identify reservoir presence, and to predict reservoir quality for applicable rock types via burial histories. On an intermediate well log to seismic scale, a predictive rock physics modeling approach links reservoir and seal rock properties to seismic amplitude data to polarize the prospect's geologic ‘probability of success'. Particular challenges in the offshore Brunei study were very fine-grained deposits and non-vertical tectonic stresses associated with compressional settings. Both make porosity predictions that leverage complex burial histories rather than relying on extrapolated depth trends quite challenging - yet the integrated approach remains the best option to identify deep reservoir quality sweetspots that a favorable stress and temperature history may have preserved for certain reservoir rock types in certain locations.
The prolific petroleum system offshore Brunei features two major sediment fairways, the Baram and Champion river systems, and a variety of depositional environments, ranging from high NtG topsets inboard over shallow marine slope settings to deepwater turbidites outboard (
Jan, Briers (Shell Global Solutions International BV) | Keat-Choon, Goh (Shell Global Solutions International BV) | Ann, Sniekers (Shell Global Solutions International BV) | Dave, Schotanus (Shell Global Solutions International BV) | John, Hofland (Shell Global Solutions International BV) | David, Adun (Shell Global Solutions International BV)
On the tenth anniversary of the first Intelligent Energy Conference held in 2006, it is appropriate to look back at some of the technologies introduced at the time, and report out on how these have been progressed. This paper discusses one of the technologies: the use of data driven models for well rate estimation, to support and enable real-time surveillance and optimization.
In the early 2000s real-time data from oil and gas fields became available in abundance on engineering central office desktops via process data historians and wide area / global communications networks. A key challenge to production management, then, as now, was: "What are the wells producing?" The April 2006 SPE Paper 99963 introduced data driven modelling for continuous well production surveillance leveraging on the suddenly abundant and often very revealing PI data.
Today, ten years later, a stream of success stories on the use real time well rate estimates based on data driven models continue to be reported, and these tools are seen as best practice in many different operational scenarios. This paper reviews the key concepts introduced in 2006, and extensions and applications of the technology to various aspects of production surveillance and optimization. As with all innovations, the most challenging elements in the journey have been related to people and processes. The paper discusses these issues in relation to the important role played by technology integration themes such as Smart Fields.
Ofonmbuk, Umanah (Alumni, Heriot-Watt University) | Shnaib, Fathi (TGT Oil Services) | Nadar, Manickam (Edinburgh Petroleum Services) | Davies, David (Heriot Watt University) | Ifechukwu, Michaels (Igbinedion University) | Olawale, Oladepo David (Covenant University)
The conventional approach of calibrating and updating well models is time consuming. Because of the dependency of the field optimization software on updated well models, the conventional approach reduces the frequency of implementing the optimizer's recommendations. To operate the fields at its maximum deliverability, taking into consideration all the system constrains, a real-time optimizer (RTO) was proposed and implemented to monitor, automatically calibrate and update well models. The question is; will the RTO and other tools be able to address these challenges?
Zuluaga, Claudia (Sarawak Shell Bhd.) | Law, Hiu Ying (Sarawak Shell Bhd.) | Jamil, Noor Azman (Sarawak Shell Bhd.) | Bashorun, Oladimeji (Sarawak Shell Bhd.) | Phua, Pei Huey (Shell Global Solutions Malaysia) | Teoh, Boon Giap (Shell Global Solutions Malaysia) | Briers, Jan (Shell Global Solutions Malaysia)
Historically, oil and gas producing fields had sufficient capacity to fulfill contractual requirements. Today, most operating companies are finding it ever more challenging to meet contractual requirements on LNG cargos and/or oil tanker loadings. For gas producing assets, the gas produced will feed LNG trains or a power plant directly. And as the feed gas needs to meet specifications on H2S, CO2, Gross Heating Value, etc… there is little room to play. Generally, this blending problem is well understood at the medium to long-term planning level. But, in day-to-day operations, there will be events that force operations to divert from the long-term plan. And this is where Real-Time Asset-Wide Optimization (RT-AWO) comes into play.
This paper details how RT-AWO was deployed and is used in a major Shell operating unit in Asia-Pacific. This asset has a complex gas-producing infrastructure where a combination of different production sharing contracts, third party producers with an ever-changing demand from the LNG plant and strict quality requirements. Having RT-AWO it is now possible to run the field in the most optimal way, all day and every day, while taking into account all possible constraints (wells, topside and pipeline).
The RT-AWO has proven to be very successful. Despite the complexity of the network, the tool is very user-friendly and easy to maintain. Not only does it allow the central control room operators to push the field to the limit, it provides them with accurate CO2 forecasts for up to 18 hours. And it takes less than a minute to compute.
The RT-AWO is a novel approach to a complex problem, sustainable, easy to maintain and use. It provides an integrated view with real-time information, forecasting and optimization functionality. And if is flexible enough to be deployed in other locations with even more complex networks.
The Champion field is a large offshore oil field in Brunei Darussalam, which has been on production since 1972. The field is complex both geologically and in terms of the production system. The reservoir has over 500 stacked sandstone reservoirs across 15 fault blocks. Over time production facilities have increased to 40 surface structures supporting production and processing from over 1300 completion intervals. Although the field is considered a mature field and with aging facilities, there is a clear long-term vision to rejuvenate the field to sustain production for another 30 years through re-development and expansion of the water injection scheme.
Ongoing maturation of the waterflood project is supported by continuous Reservoir Performance Reviews (RPRs). The RPRs bring together the reservoir understanding, hydrocarbon saturations and production data. The purpose of the RPRs is to identify opportunities – restoration, acceleration and reserves addition and compile them in an integrated development plan covering the short, mid and long term. RPRs are carried out at the flow unit level - a small group of commingled reservoirs within a single fault block.
Top Quartile Recovery Factor (TQ RF) analysis is an integral part of the RPR. With the Top Quartile Estimated Ultimate Recovery (TQ EUR) tool, Shell developed a benchmarking tool which provides a conceptual framework that probes the performance of each field in a consistent manner. Basic geological and fluid properties as well as economic parameters are used to determine a complexity factor for each flow unit. The current and expected ultimate RF of the unit is then compared to the TQ RF (from reservoirs of the same complexity).
A systematic approach is taken to bridge the gap to TQ RF. Closed-in well reviews identify restoration and water injection candidates. Deterministic analysis of sweep patterns and high remaining oil saturations are compared with streamlines in the NFA forecast model. Potential gains are quantified by the simulator. The outcomes are near-, mid- and long-term surveillance and development plans to move the recovery up to TQ and beyond to the technical limit (TL).
By applying this systematic RPR/TQ process per flow unit across the whole field, a detailed reservoir understanding of this giant, complex field is achieved. This approach enabled the integrated WRFM team to deliver significant value in terms of increased production, optimization of Waterflood development and new in-well and infill opportunities.
The Champion field is a large mature offshore oil field in Brunei Darussalam, operated by Brunei Shell Petroleum (BSP). It is the largest oil field in BSP’s portfolio. It was discovered in 1970 and went on production in 1972. The field is located about 40 km offshore in water depths of 10 - 50 m (Figure. 1).
Saputelli, Luigi Alfonso (Frontender Corporation) | Bravo, Cesar (Halliburton) | Nikolaou, Michael (University of Houston) | Lopez, Carlos (BP) | Cramer, Ronald (Shell) | Mochizuki, Satoshi (Consultant) | Moricca, Giuseppe (Consultant)
In the last decade, upstream oil industry faced an exponential increase of the use of real-time data, which lead to numerous digital oilfield (DOF) implementations. These have demonstrated the value to drive operations efficiency, optimize production, and maximize hydrocarbon recovery with better, faster decisions while reducing health, environmental and safety risks.
Since the appearance of computers and the internet, many enabling technologies entered the oil-patch. Over the years, various areas improved as a result of significant commercial, corporate and academic efforts. However, some specific concerns remain be the same as a decade ago: data, value proposition, work processes, people skills and other aspects of change management.
This paper focuses on the best practices that have made DOF implementations successful and the hard lessons learned. Many DOF implementations failed to deliver the expected value because of poor practices and misconceptions. These are presented in four interrelated areas: people, automated workflows, processes and technologies.
Management of change at various levels of the organization continues to be a critical success factor, e.g. the introduction of new systems, designing effective collaborative ways of working, training people, management support and having the right resources (time and financial) to focus on real-time oil and gas production optimization.
Automated workflows have been developed and deployed at various intensity levels. Although there is a lack of common language for defining workflows, there has been tremendous improvement in this area.
On the other hand, work processes had been an area of little improvement. Because of poor process definition and understanding, companies failed to adopt and sustain highly sophisticated workflows. In the paper, we discuss the effort required to properly describe work process that support DOF implementations. We also describe the technology challenges faced and how these can be mitigated in the future.
Oil and Gas Information Management - Major challenges
Engineering and operations personnel are increasingly absorbed in day to day activities. The stress to collect and filter data, and deliver daily reports tends to intensify compartmentalized behavior and to discourage information flow between key processes that should be more integrated. For example, data on the availability of injection gas from compression facilities may not be integrated with the anticipated needs and performance of injection and producing wells and with the characterization of a reservoir. The limited visibility of the impact of equipment availability on overall asset performance does not allow continuous operational optimization in response to dynamically changing field conditions.
Work force demographics and dynamics also present challenges to integration. For example, entire asset teams with their own processes and tools frequently merge with a new, parent operating company. The operating company must then assimilate these teams into their organizational structure via a cumbersome balance of centralized and distributed authority, an assimilation process that is further aggravated by asset-level, profit-loss accountability and authority.
Although basic infrastructure or technological elements such as instrumentation, communications and surveillance software are no longer common obstacles or challenges to overcome, as they were a decade ago, just one missing element prevents the others to perform at their best.
This paper describes the implementation of an optimization advisory system by Sarawak Shell Bhd. on theintegrated gas production system in Sarawak, to help monitor and optimize in real-time the more than 100 wells on 30+ facilities of the system, governed by different Production Sharing Contracts. We highlight how multiple disciplines are part of the solution, how the solution has been embedded to provide real-time advisory to planners
and operators, and the challenges faced. The technology described in this paper provides real-time advisory on flow rates for individual wells and platforms to network coordination operators to achieve a gas supply that meets overall gas demand for the onshore LNG plants, maximizes revenue from condensates production and CO2 blending, and transparently shows the business value impact of giving preference to short- or long-term optimization objectives. It takes into account existing commercial and contractual constraints, as well as financial and economical elements. The optimization approach is data-driven and features multiple, mutually dependent objectives and constraints. The data-driven approach has proven significantly faster and more practicable than prior physical model-based approaches. Field trials have proven a condensate production increase of up to 7,000bbl/d while at the same time improving Ultimate Recovery of the sweet and sour gas blend of the production system by up to 2 months per ‘year of applied optimizer advise'. At the same time, gas nominations are met as they were, and commercial and quality constraints are adhered to. The Return On Investment of this solution has been less than a month. This is one of the first successful attempts to implement truly-real-time optimization in a production environment of this size and complexity, including a complicated set of commercial and contractual constraints. The approach opens the door to application to a wide variety of real, complex, mixed-integer non-linear optimization problems, and their organizational application, such as emissions management and decision support systems for integrated IOR/EOR systems.
Some Majors have indicated that they will drill up to 20,000 new wells by the end of the decade. To achieve well numbers and cope with the work load and manpower shortages, drilling automation technologies are being introduced that will allow multiple rigs to be operated remotely and downmanned. It is premised that well production operations will also have to be automated to cope with increasing work load as resource will not be available to operate these wells in the traditional, manual way. Associated production processes that will have to be automated include, well surveillance/optimization, well testing, sampling, chemical injection and hydrocarbon accounting. It is desirable to bring the wells onstream as soon as possible after drilling and to maintain production rates at as high a level as possible and to simultaneously account for production of oil, gas and water. It also is imperative to ensure the highest level of safety and environmental factors. Hence, the purpose of this paper is to describe well automation business requirements/benefits and potential system solutions to optimize production over the life cycle for the ever increasing number of onshore wells that will be drilled in the near future.