A common problem in the E&P industry is that most of the assets produce well below their installed potential. Reasons vary from suboptimal well/equipment set points, flow bottlenecks, or inefficiently managed operational anomalies. This gap, however, also presents E&P operators with opportunities for cost-effective production gain and increased cash flow. To that end, digitalization of production operations now gathers urgency as E&P operators seek to improve their ability to reduce performance gaps through proactive asset surveillance and high-impact production optimization decisions, executed on a daily basis.
Fig: Anatomy of Performance Gaps in E&P Production Operations
That said, applying digital technology in the E&P business is hard work. There is no single universally applicable implementation blueprint as different producing assets vary in their digital maturity, organization skillsets, and operational workflows, with the added challenges of non-heterogeneous software systems, fragmented data environments, and siloed decision making.
Halliburton, the leader in the digital oil field space, understands these challenges well. Having delivered over 30 digital projects across 50+ hydrocarbon producing assets globally, we have mastered the art of designing solutions that are easy to use and maintain, work with operators’ existing operational and IT environments, and scale to their future needs. Such depth of experience gives our clients confidence in higher returns on their digital investment. Our recent partnership with an Asian National Oil Company (NOC) is an example of that.
The NOC tapped Halliburton to implement a digital transformation initiative in three of their major offshore field assets producing gas and condensate. Having entered their late production life, the fields are exhibiting typical mature field characteristics such as production decline, integrity issues, and a rise in operational complexities.
In terms of challenges, the operator felt they were lacking the ability to rapidly respond to changes in operating conditions (e.g. water breakthrough, breakdowns) and customer demand. In addition, flow bottlenecks and process inefficiencies created gaps between well potential and production output at sales points.
Further compounding the problem, the highly complex nature of reservoirs makes it difficult to model well behavior using nodal analysis tools, resulting in limited understanding of the backpressure effects across surface networks and subsurfaces. The engineering models for processing equipment (e.g. compressors, condensate stabilization units) are in place but data deficiency makes them unfit for use.
Fig: Challenges faced by the National Oil Company
The digital initiative addresses these challenges through a combination of technology and creative problem solving. A key goal for the operator is to achieve dynamic optimization capability through live and up-to-date performance models (or digital twins) of the production systems. This would allow them to adjust the operating set points of the wells and equipment to optimize product yields and prevent excursion of sand and water from the wells.
A scalable and vendor-agnostic digital platform was considered necessary to connect with wide range of data streams and deliver purpose-built digital twins and visualization to automate 10 surveillance and optimization workflows. The latter includes Sand Prediction, Flow Assurance, Pressure-Rate Correlation, Suction Pressure Optimization, Corrosion Inhibitor Injection Management, and Condensate Stabilization, to name a few.
The solution proposed by Halliburton is DecisionSpace® 365 Production Suite, a cloud-based solution that offers rapid deployment and flexibility to tailor digital solutions to the specific needs of hydrocarbon producing assets. Its open, technology-neutral, modular, and interoperable architecture enables seamless orchestration of performance models and integration with disparate data sources. The underlying technology building blocks (e.g. data connectors, business rules, data-driven models, and visualization) accelerate the solution deployment.
Fig: DecisionSpace® 365 Production Suite in Action
For each workflow, the DecisionSpace 365 Production Suite will deliver a digital twin using a blend of first principle and machine learning techniques - the latter necessitated by lack of data or inadequacies of the former since good quality data is key to maintaining trust in the models. The platform will ingest, clean up, and transform the data streams from sensors and non-real time systems before feeding them into the models.
The workflow orchestration engine will automate repetitive tasks and engineering calculations, minimizing end users’ involvement in data QA/QC and model management activities. All this machinery will work efficiently under the hood letting end users run their workflows and interact with data through user-friendly visualization.
The workflows will be delivered in an agile manner using the concept of Minimal Viable Product for continuous learning and improvement. Halliburton will complement this approach with our SmartDigital™ co-innovation service, which applies design thinking to E&P engineering challenges.
As digital transformation progresses in the two assets, even greater business value can be unlocked by harnessing the scalability of the DecisionSpace 365 platform, adding new capabilities such as integrated planning and predictive maintenance, and making the power of the platform accessible to other producing assets.