Unconventional reservoirs have characteristics that differ from traditional conventional reservoirs. The productivity profile of an unconventional well can be significantly different from a conventional well, in that the production rate declines faster in an unconventional well. Therefore, properly planned well operations are crucial to optimize costs and production from these shale assets. This includes understanding the reservoir physics, planning optimal well spacing, improving well performance from completions, and simulating refracturing effects on well production. This paper presents a new multidisciplinary method to help improve field performance and productivity in unconventional reservoirs. A multidisciplinary approach is necessary for economical and successful operations in unconventional reservoirs. In unconventional fields, wells are drilled quickly; therefore, rapid decision-making is necessary. Currently, fracture modeling is performed using either fine local grid refinements or dual-porosity dual-permeability models, which can be cumbersome and time-consuming. This paper presents a new approach that uses multiple shale-specific features and unstructured models, which allows users to specify discrete natural fracture networks (NFNs) and hydraulic fractures with arbitrary orientations connected to practical well trajectories. The automated gridding technique significantly simplifies the workflow, thus allowing users to focus on addressing issues in the engineering space by streamlining the setting up of complex reservoir simulation models. The approach is applicable to black oil and compositional models of all fluid types. Using parallel capability, performance can be enhanced severalfold. The new approach helps enable modeling of multiple scenarios by modifying parameters easily; thereby, results are readily available to help operators plan optimal well and fracture spacing and length. This paper highlights how well productivity can be improved by optimizing well placement and incorporating the effect of NFNs and hydraulic fractures.
Oil and gas operators continue to seek better ways to unlock value from operational data and drive consistent, predictable performance while mitigating risk on a well-to-well basis. This paper describes how a wellsite Edgecomputing solution was developed to connect and orchestrate the well-activity plan with wellsite advisory systems, provide unified instructions for drilling automation systems, improve the human-to-system interface, and connect cloud-based data lakes to real-time wellsite operations. Crucially, the system leverages open technologies and frameworks to expand options and provide a low-barrier entry point to automation for all vendors. In this new environment, multiple engineering applications can execute in parallel in a distributed microservice-based system ensuring the most pertinent models are continuously leveraged and anchored to the current operational situation. The engineering outputs are then orchestrated against current and future operational context to manage drilling limiters while anticipating and mitigating possible dysfunctions and inefficiencies. As automation has become a mainstream technology in well construction, there is a need for open platforms that integrate both well-activity plans and engineering systems into automated decision-making rig systems. This paper provides details on how this gap has been closed through a vendor-agnostic platform, which aggregates high-frequency, low-latency real-time data with well-planning information and hybrid data-driven models to provide closed-loop control and context-based decision making that interoperates directly with surface and subsurface drilling-automation systems.
Objectives/Scope Drilling operations rely on the collaboration of many participants, and the efficiency of this collaboration depends on timely exchange of information. The complexity and variability of this information make it difficult to achieve interoperability between the involved systems. Recent industry efforts aim at facilitating the many aspects of interoperability. A central element is semantic interoperability: the ability to correctly interpret the real-time signals available on the rig. This contribution presents an implementation of semantic interoperability using OPC UA technology. It translates the principles developed through joint industry efforts into actual drilling operations. Methods, Procedures, Process The process used the steps of characterizing the drilling real-time data with semantic graphs, and then developing methods to transfer this characterization to an operational real-time environment. A semantic interoperability API (application programming interface) uses the semantic modelling capabilities of OPC UA. Its objectives are to facilitate the acquisition and identification of real-time signals (for data consumers) and their precise description (by data providers). The different components of the API reflect the diversity of scenarios one can expect to encounter on a rig: from WITS-like data streams with minimal semantics to fully characterized signals. The high-level interface makes use of semantical techniques, such as reasoning, to enable advanced features like validation or graph queries. Results, Observations, Conclusions The implementation phase resulted in a series of open-source solutions that cover all the stages of semantic interoperability. The server part integrates real-time sources and exposes their semantics. Data providers can use dedicated applications to accurately describe their own data, while data consumers have access to both predefined mechanisms and to more advanced programming interfaces to identify and interpret the available signals. To facilitate the adoption of this technology, test applications are available that allow interested users to experiment and validate their own interfaces against realistic drilling data. Finally, demonstrations involving several participants took place. The paper discusses both the testing procedures, the results and insights gained. Novel/Additive Information The solutions described in this contribution build on newly developed interoperability strategies: they make on-going industry efforts available to the community via modern technologies, such as OPC UA, semantic modelling, or reasoning. Our hope is that the adoption of the developed technology should greatly facilitate the deployment of next generation drilling automation systems.
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