For E&P companies, the digital twin concept is a fundamental element of managing integrated production and reservoir systems. Because of its rapid acceptance across various industries, the International Data Corp. expects that this emerging digital technology will help companies gain a 30% improvement during cycles of their critical processes. For the oil and gas industry, the digital twin is the “information vehicle” that enables systems to exchange vital information among multiple integrated production and reservoir subsystems, driving the highest possible recovery using the most efficient and cost-effective operation.
Reservoir and production systems are closely tied, so reservoir performance has traditionally been used as an input to model the well’s ultimate recovery efficiency under various development scenarios. A comparison of the rates of return from different production approaches and recovery methods can be made by using this model. During operations, the production history is matched with reservoir simulation models to establish the models’ validity. Both production and reservoir models are used to assess scenarios for the reservoir’s future development.
While oil and gas system methodologies and technologies have improved and become more integrated, a corresponding increase in automation complexities has occurred. Equally important, daily generated performance and health-state information have become vital assets for these integrated systems to manage. This ever-increasing tide of data has caused new requirements on its governance and flow to maintain visibility and control. Interoperability of data and cross-domain workflows is extremely important while organizations stress efficiency and intelligent decision-making.
Traditional monitoring solutions fill large historical and real-time repositories. While 24-hour monitoring centers help improve operational efficiencies, they are susceptible to human error. Digital twin solutions can be used to more accurately sense, diagnose and help optimize the physical twin’s behavior to address this challenge.
A digital twin for integrated reservoir and production management is an ideal solution for managing complex integrated oil and gas production and reservoir systems. Because the digital twin seamlessly exchanges vital information among the various integrated production and reservoir subsystems, it is instrumental in addressing the problem of information-flow complexity. Additionally, the digital twin can provide the health-state for integrated systems, enabling both preventive and remedial actions. As is often the case with optimization opportunities, the more vertically integrated the assets, the easier it is to capture all the benefits resulting from a systemwide digital twin approach.
According to Dr. Michael Grieves, an expert on product life-cycle management, there are various implementations of a digital twin. One is digital twin instance, with the asset and current sensor measurements replicated “as is.” Another implementation is digital twin aggregate, which involves providing an aggregation of multiple instances. A third implementation is a digital twin prototype, which provides a multidimensional representation of a producing asset, not a specific replica.
Landmark, a Halliburton product service line, has developed a digital twin for integrated reservoir and production management that leverages the enterprise capabilities of Landmark’s DecisionSpace platform. Implementing an aggregate twin to address challenging problems provides a solution that helps manage integrated system complexities.
The full benefits of implementing digital twins are realized when they are part of a comprehensive digitalization strategy customized to the field (for example, legacy fields versus new developments). In addition to digital twin technology for integrated production and reservoir systems, Landmark is developing digital twins for well construction and for exploration and reservoir management.
For both conventional and unconventional scenarios, a digital reservoir twin provides comprehensive solutions to maintain the integrity of geological features, helps improve accuracy, increases efficiency and maximizes the return on investment for assets.
The reservoir production digital twin enables multiple workflows, including integration analysis, that use part or the entire DecisionSpace technology suite. Some of these fundamental workflows include
• Field allocation optimization that provides optimization for an entire oil or gas field by calculating each well’s production capability at different time steps to obtain optimum field production;
• EOR to optimize production from each well and maximizes the ultimate recovery based on enhanced recovery mechanisms; and
• Holistic field development to determine technically and economically feasible field development scenarios from early stages to mature field to abandonment.
The fundamental objective is to provide a representation of a single asset that can be optimized in collaboration with reservoir, production and completion engineers as well as with the field operations team.
Waterflood optimization is an example of the implementation of an integrated reservoir production digital twin. One example of a digital twin is a smart water optimization workflow used for a national oil company in the Middle East that enables optimization of water sweep and utilization efficiency using smart coupling of wells and subsurface models, or digital twins, with reactive and semi-proactive optimization strategies (Figure 1).
SMART WATER OPTIMIZATION WORKFLOW
This workflow comprises two sub-workflows executed in two different time frames: the first sub-workflow is focused on short-term reactive optimization of settings for surface chokes and water injection rates, while the second targets mid- to long-term proactive optimization, which supports field development planning for workover actions and new well types and locations.
The primary objective of the smart water optimization is to develop an automated workflow to monitor, diagnose and optimize waterflooding process systems using an intelligent, real-time control process to provide proactive recommendations for water injection and production systems, thus maximizing oil recovery and reducing water production.
By combining real-time data and Big Data analytical models, digital twins reflect the actual conditions of its physical twin, allowing operators to decide between multiple constraints to provide meaningful operational insights and enable effective workflows (Figure 2).
Digital twins for integrated reservoir systems enable reservoir management and production teams to collaborate on a single integrated model. This comprehensive solution allows all asset team members to interact with a common and unified vision of the asset. Multireservoir assets where individual reservoirs are connected through a shared network especially require such a solution because how each reservoir is operated impacts the production of the other reservoirs. An integrated multireservoir model can accurately determine the interactions of these reservoirs, causing decisions that help improve production, ultimate recovery and asset life.
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