Digital oilfields

The purpose of the digital oilfield is to maximize oilfield recovery, eliminate non-productive time, and increase profitability through the design and deployment of integrated workflows. Digital oilfield workflows combine business process management with advanced information technology and engineering expertise to streamline and, in many cases, automate the execution of tasks performed by cross-functional teams.

Overview

The term "digital oilfield" has been used to describe a wide variety of activities, and its definitions have encompassed an equally wide variety of tools, tasks, and disciplines. All of them attempt to describe various uses of advanced software and data analysis techniques to improve the profitability of oil & gas production operations. Frequently recurring digital oilfield themes include—but are not limited to:

• Operational efficiency
• Production optimization
• Collaboration
• Decision support
• Data integration
• Workflow automation

One way to understand the rise of digital oilfield technology is by considering some of the unprecedented challenges currently being faced by the oil and gas industry:

• Bimodal age distribution of workforce ("crew change")
• Proliferation of software applications and data formats
• Global distribution of work teams
• Instant availability of massive amounts of real-time data
• Steadily decreasing number and size of new discoveries
• Growing expense of advanced recovery technologies

If one maps the challenges onto the themes, it becomes clear that digital oilfields are attempting to compensate for a higher complexity and cost of operations which must be performed by fewer, less experienced employees. To achieve this, digital oilfields must either subsume or accelerate many of the tasks and processes traditionally performed by engineers, geoscientists, field technicians, financial analysts, and even managers. The industry has come to refer to ordered, related collections of such tasks as workflows, and industry professionals increasingly think in terms of workflow design.

The design of workflows and processes is a discipline that historically has most often been practiced by industrial engineers or operations management specialists. The language of oil & gas production has changed to reflect the adoption of their techniques. Traditional segmentations of responsibilities among functional lines (e.g. reservoir, completion, and production engineers) are being integrated into engineering workflows and business processes that more broadly reflect the corporate objectives to be achieved (e.g. reservoir surveillance, well test validation, production optimization). Digital oilfields, in one sense, comprise sets of workflows that allow fast, collaborative execution of interrelated tasks among distributed (virtual) teams, with an end result that is optimal, efficient, and more profitable.

History

The software, information technology and engineering advancements that have spurred digital oilfield adoption by the industry have largely grown out of initiatives which started in earnest around the turn of the century. However, one of the earliest targets for workflow orchestration was the integrated modeling of flow through a pressure-connected network of reservoirs, wells, and surface production facilities. This type of integrated modeling was first introduced in 1976 .

Alternately termed "tightly coupled reservoir-wellbore-surface network simulations" , "integrated production models" or "integrated asset models" , the publication of case studies using these techniques began to increase awareness of the benefits of collaboration and of workflows that crossed functional lines. Engineers working in different disciplines began to be able to foresee potential effects of their decisions on other parts of the production network (e.g. a production engineer could see just by running a workflow that the facilities engineer did not have enough separation capacity to handle an increased well flow rate).

Another early benefit realized through integrated asset modeling was the ability to account for the uncertainty of model inputs by generating probability distributions for uncertain inputs and using Monte Carlo analysis to generate an equivalent distribution of outputs, along with their expected values . In general, whether deterministic or stochastic, the ability to automate the execution of multiple scenarios was immediately seen as a key advantage of integrated asset modeling, one that continues to influence the design of new workflows.

As the maturity of workflow designs, the experience of digital oilfield users, and the availability of computing power all increased, digital oilfield implementations began to actively optimize desired results within a given set of constraints, rather than simply provide distributions of scenario results. In this way, digital oilfields began to be used more and more for decision support, and there was a coincident drive to obtain workflow results faster in order to maximize the potential cost savings.

In many cases, it was not the time required to execute engineering models that was delaying decision support from workflows: engineers were spending excessive amounts of time finding, organizing, processing, entering, and validating data—all before any analysis at all could be performed. Therefore, digital oilfields also had to address data management problems . Digital oilfield providers began to speak of "technology integration" alongside workflow integration and model integration. Data sources had to be developed to load, store, clean, process, and validate data coming from a diverse array of sources, from legacy software file formats, to relational databases, to email, to PDF, and more. All of this data had to be quickly accessible for use in any workflow without regard to its point of origin.

Furthermore, organizations had to begin learning to use these systems effectively, and change management necessitated the development of business process management (BPM) tools. BPM gave corporations the ability to enforce best practices, first through the design of workflows by subject matter experts, and then through the subsequent execution of those workflows by all personnel in conjunction with a system of flowchart-based process diagrams, automated email notifications, and mandatory review-and-approve steps. As such, BPM continues to be an important part of any successful digital oilfield deployment.

The growing influence of the Internet also affected the design of digital oilfield systems. As corporations grew more confident in their use of digital oilfields, they began to desire greater standardization of their systems across assets and regions. Web-based visualization environments became important ways to lower IT costs and to provide widely dispersed teams with a shared vision of models and data. Software as a Service (SaaS) became a valuable way for digital oilfield providers to enable precisely the functionality needed by a customer, in a way that was simple for IT administrators to deploy globally, and at any scale .

Future Trends

Great strides have been made over the past ten years in the design, deployment, and use of digital oilfields. Many of the lessons learned were hard ones, and today some of the greatest difficulties are on the people side of the business: management of change, personnel skill development, design of business processes, and communication of the value proposition to be gained .

Although the basic technology integration elements required to deploy a modern digital oilfield are generally available, the rapid and constantly-increasing pace of technology change now presents unique challenges—and opportunities. For instance, whereas at the turn of the century a challenge was instrumenting wells and facilities in order to obtain usable production data, today's challenge is developing simulations and optimization workflows that can consume the massive rate of incoming data, filter it, process it, execute models, perform analysis, and recommend actions to decision-makers—all in real-time . The "data revolution" also now makes it possible to construct many new types of models, data-driven or "proxy" models that enable engineers to predict system behavior for which trusted physics-based models are either too time-intensive or perhaps even non-existent.

Additional scope for digital oilfields is expected to eventually encompass closed-loop, autonomous control of operating facilities, a practice that is widespread in most manufacturing environments, and even in the downstream sector of the industry. Rig automation and drilling automation will be used for oil field manufacturing. Clearly, attention to health, safety, and the environment will be top of mind issues for operators who lead this transformation of the industry. As such, digital oilfields will become increasingly concerned with operational efficiency and the optimization of processes that are not directly related to the core petroleum engineering activities for which they are currently used.

As digital oilfields expand horizontally to encompass every aspect of operations and engineering, they will also expand vertically within the organization to touch every functional discipline, from accounting and finance, to executive management. Digital oilfields will become automated oilfields, and within will be automated companies, with all information pertaining to the acquisition, development, production, and disposition of oil & gas assets being managed in a centrally-administered system with BPM processes, orchestrated workflows, and notifications. Changes to a production plan in one asset will, through the design of increasingly more sophisticated workflows that include economic analysis, roll up to a revised portfolio optimization plan maintained by the Finance Department, with changes in expected Net Present Value being made immediately available to executive decision-makers.

The arrival of automated companies will create an environment in which the financial impact of individual technical decisions becomes transparent to all stakeholders. Financial metrics will, at that point, influence the objective functions used to optimize technical operations. At that point, automated oilfields will be indistinguishable from the most advanced factory operations; lean, streamlined, efficient.

Noteworthy papers in OnePetro

Dickens, J., Feineman, D., & Roberts, S. (2012, January 1). Choices, Changes and Challenges: Lessons for the Future Development of the Digital Oilfield. Society of Petroleum Engineers. http://dx.doi.org/10.2118/150173-MS

Feineman, D. R. (2014a, April 1). Assessing the Maturity of Digital Oilfield Developments. Society of Petroleum Engineers. http://dx.doi.org/10.2118/167832-MS

Feineman, D. R. (2014b, April 1). Digital Oilfield Implementation: Learning From the Ghostbusters. Society of Petroleum Engineers. http://dx.doi.org/10.2118/167831-MS

Kamal, S. Z., Williams, J., & Liddle, J. (2014, April 1). Continuous Improvement of Assets Through Existing and New Digital Oilfield Technology. Society of Petroleum Engineers. http://dx.doi.org/10.2118/167908-MS

Shelley, B., Lehman, L., and Grieser, B. (2004). Holistic Field Evaluations Improve Prospect Opportunities, Society of Petroleum Engineers, http://dx.doi.org/10.2118/88530-MS

Boisvert, I., Strobel, M. A., and Szatny, M. (2012). A Novel Tubing Movement Workflow Increases Efficiency and Planning for Deepwater FracPack Operations, Society of Petroleum Engineers, http://dx.doi.org/10.2118/155867-MS

Machado, L., Costa, M., Correa, S., Regina, E., and Herdeiro, M. (2012). Enhance Platform Integrity by a Real-Time Equipment Availability Monitoring Workflow, Society of Petroleum Engineers, http://dx.doi.org/10.2118/152330-MS

Dusterhoft, R., Strobel, M., and Szatny, M. (2012). An Automated Software Workflow To Optimize Gulf of Mexico Lower Tertiary Wilcox Sand Reservoirs, Society of Petroleum Engineers, http://dx.doi.org/10.2118/151754-MS

Bravo, C. E., Saputelli, L., Rivas, F., Perez, A. G., Nickolaou, M., Zangl, G., De Guzman, N., Mohaghegh, S., and Nunez, G. (2013). State of the Art of Artificial Intelligence and Predictive Analytics in the E&P Industry: A Technology Survey, Society of Petroleum Engineers, http://dx.doi.org/10.2118/150314-PA

Pickering, J., Shields, J., Gidh, Y., Johnson, D., Shehry, M., Khudiri, M., Farnan, M., and Schey, J. (2012). WITSML: Laying the Foundation for Increasing Efficiency of Intelligent Wellsite Communications, Society of Petroleum Engineers, http://dx.doi.org/10.2118/150278-MS

Strathman, M. and Lochmann, M. (2012). Intelligent Energy and Other Industries - Lessons Learned, Society of Petroleum Engineers, http://dx.doi.org/10.2118/150152-MS

Lochmann, M. (2012). The Future of Surveillance, Society of Petroleum Engineers, http://dx.doi.org/10.2118/150071-MS

Davidson, J., and Lochmann, M. (2012), Lessons and Insights from Unexpected Places, Society of Petroleum Engineers, http://dx.doi.org/10.2118/149998-MS

Saputelli, L., Perez, J., Chacon, A., Lopez, C., Patino, J. and Eggenschwiler, M. (2010). Well Productivity Index Degradation: Applied Modeling Workflow, Society of Petroleum Engineers, http://dx.doi.org/10.2118/133452-MS

Garcia, A., Rebeschini, J., Martins, D., Vieira, C., Nunes, F., Da Silva, E., and Herdeiro, M. (2010). Enhanced Reservoir Scenarios Management Workflow, Society of Petroleum Engineers, http://dx.doi.org/10.2118/132983-MS

Teotico, D., Schauerte, L., Griffith, J., Schottle, G., and Mehl, R. (2010). Simulations of Field-Development Planning Help Improve Economics of Heavy-Oil Project, Society of Petroleum Engineers, http://dx.doi.org/10.2118/124203-PA

Paulo, A., Taylor, D., Isichei, O., King, M., and Singh, G. (2011). Transforming Operations with Real Time Production Optimization and Reservoir Management: Case History Offshore Angola, Society of Petroleum Engineers, http://dx.doi.org/10.2118/143730-MS

Saputelli, L., Ramirez, K., Chegin, J., and Cullick, S. (2009). Waterflood Recovery Optimization Using Intelligent Wells and Decision Analysis, Society of Petroleum Engineers, http://dx.doi.org/10.2118/120509-MS

Adeyemi, O., Shryock, S., Sankaran, S., and Hostad, O. (2008), Implementing "I Field" Initiatives in a Deepwater Green Field, Offshore Nigeria, Society of Petroleum Engineers, http://dx.doi.org/10.2118/115367-MS

External links

DiFiore, Amanda. "Human Factors in Automation." : Web Events. Society of Petroleum Engineers, 22 Sept. 2015. Web. https://webevents.spe.org/products/human-factors-in-automation.

Popa, Andrei. 2015. "Understanding the Potential of Case-Based Reasoning in the Oil Industry." Web Events. Society of Petroleum Engineers, https://webevents.spe.org/products/understanding-the-potential-of-case-based-reasoning-in-the-oil-industry-morning-session.

Industry Groups and Associations

SPE Intelligent Energy International: http://www.intelligentenergyevent.com/

SPE Digital Energy Study Group: http://www.spegcs.org/study-groups/digital-energy/

Center for Integrated Operations in the Petroleum Industry: http://www.iocenter.no/

Articles of Interest

Hart Energy - Integrating the Intelligent OIlfield: http://www.epmag.com/EP-Magazine/archive/Integrating-Intelligent-Oilfield_6264

Examples

Chevron - Digitizing Oil Fields: http://www.chevron.com/next/digitizingoilfields/

BP - Field of the Future: http://www.bp.com/en/global/corporate/about-bp/bp-and-technology/more-recovery/real-time-data-and-decisions/field-of-the-future.html

KOC - Kuwait Integrated Digital Field: http://kwidf.kockw.com/Default.aspx?alias=kwidf.kockw.com/kwidf

See also

Digital energy

Drilling automation

Intelligent wells

Reservoir management

Drilling data management

PEH:Intelligent_Well_Completions

Data driven approaches to production management