Lessons from Digital Oilfield Initiatives
- Written by Amol Bakshi and Viktor K Prasanna
Digital oilfield initiatives undertaken in the last decade have required oil and gas companies to confront a number of issues and challenges. Drivers of value and approaches to solutions are fundamentally similar to those in the smart grid.
Oil and gas companies have been investing in digital oilfield initiatives to improve performance of existing and future oilfield assets, reduce capital expenditure and operating costs, and better utilize the capabilities of a limited number of experienced personnel.
Increasingly, oil and gas discoveries are being made in remote and hostile environments, making it highly desirable to leverage information and communication technologies. The idea is to facilitate remote operation and reduce or eliminate the need for personnel to go out into the field for routine data collection and inspection activities.
In addition, highly instrumented assets and the capability to collect, analyze and visualize information in real time have enabled fundamentally new cross-functional and collaborative workflows, so that there can be asset-level decision making and optimization across the entire value chain from the hydrocarbon reservoir to product delivery.
The business motivations for utilities to invest in smart grid initiatives are not too different: the value in outfitting transmission and distribution networks with instruments; using real-time data collected from the sensors to enable proactive monitoring and maintenance workflows; employing analytical techniques to model and predict demand profiles and optimize generation assets; and optimally managing the "big crew change" as experienced workers retire and new hires fill the roles.
One of the key lessons from digital oilfield projects is that the quality of decisions from surveillance, analysis and optimization workflows is ultimately dependent on the quality of the data collected from the field as well as the ability to communicate data in a consistent manner between a large number of IT systems that were never designed to be interoperable. A lot of effort is required to address data quality issues (unreliable sensors, statistical outliers, missing data, and so on), and to design and agree on data exchange standards across vendor products. This effort involves not just the programmers and architects of the software systems, but also the subject matter experts and potential end users. The eventual success of the smart oilfield (or smart grid) solution all depends on resolution of fundamental issues around data quality.
There are opportunities for the Smart Grid community to leverage much of the work done by standards organizations in the O&G industry, such as Energistics. For instance, the PRODML Product Flow Network schema is a scalable model that represents a producing oil and gas asset in the form of a graph. The graph models the flow of product (oil, gas, water) from the reservoir, through the well bore, into the gathering system, and through the processing facilities.
Having such a common representation of the entire system enables diverse applications to communicate information about the same physical entity (such as a particular well head) while still maintaining their own internal representations, naming conventions, and the like. A variety of joint pilot projects between oil and gas companies, oilfield services companies and software vendors have helped define the business case and specifications for such standards.
Another relevant observation from the digital oilfield experience is that non-technical aspects of training, change management and support are in many cases more critical to the success of an integrated IT solution than the technical capabilities it offers. The portfolio of digital oilfield projects pursued to date has many examples of systems and solutions that could be considered successful from the purely IT perspective, but have little or no usage among the targeted end users because stakeholder engagement, communication and training activities were not adequate.
With integrated solutions comes the need for a new type of skill set among users and support personnel. In the digital oilfield world, there is a growing need for engineers who combine petroleum engineering knowledge with relevant IT knowledge. The oil and gas industry is collaborating with universities to define appropriate ways to augment existing curricula in both these domains so that the next generation workforce is equipped with the right skills to perform their roles. We expect that the smart grid community will encounter the same challenge of training the new workforce. Lessons from the oil and gas industry should be valued.