Formalization and Standardization of the Smart Well Valuation Workflow
- John Dale Hudson (Shell) | Ibere Nascentes Alves (Petrobras) | Mohammad Khoshkbarchi (Computer Modelling Group Inc)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 30 October-2 November, Denver, Colorado, USA
- Publication Date
- Document Type
- Conference Paper
- 2011. Society of Petroleum Engineers
- 4.3.4 Scale, 5.5.8 History Matching, 5.5.1 Simulator Development, 5.5.5 Evaluation of uncertainties, 4.1.5 Processing Equipment, 5.1.5 Geologic Modeling, 2.3 Completion Monitoring Systems/Intelligent Wells, 5.5 Reservoir Simulation, 3.3.6 Integrated Modeling, 5.6.4 Drillstem/Well Testing
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Case studies and related to approximately 30 oil and gas production fields containing smart wells were reviewed to consider the work process that was used to justify the incremental investment in hardware and installation cost. These fields are from two operating companies and are distributed across most major oil and gas productive regions worldwide. The paper outlines key findings from the review, recommends a project-stage-based modelling workflow and presents opportunities for improvements to support more rigorous and efficient design decisions.
In recent years wells with smart completions* have gone from oddities to a relatively common development option, and from technology demonstrations to, in some cases, solid value deliverers. The processes for evaluating such opportunities, for defining and executing projects to implement field developments using these technologies and ultimately for operating the fields have rapidly matured during this time. Within these processes we are now able to consider many aspects that affect field profitability and ultimate recovery, including geological features and uncertainties, well and facility design, operational philosophy and procedures and risks. Consideration of these topics is inherently multi-disciplinary, and the quantification of often very detailed technical analysis must be consolidated into an overall (typically economic) "model?? that can be used for decisions. Numerical simulation of the production system has historically played a key role in supporting the required incremental investments for installation and hardware, and these simulations can now include many of the relevant technical factors.
A growing number of investigations have described the evaluation process for determining the value of smart wells, including diverse project and well characteristics. These investigations typically also give some detail of the analytical tools that were used. While there is some commonality in these approaches, standard and efficient work processes for such evaluations are still evolving. Given several years of production experience with numerous smart wells, it is now timely to review the processes of justification of smart well investments along with their historical benefits, with the aim of improving an organization's ability to predict actual benefits.
This paper considers the modeling workflows and valuation processes used by and experiences of two major operators, Shell and Petrobras, across a wide variety of implementations globally, including onshore, offshore and subsea type developments. The fields are associated with approximately 150 smart wells, many of which are already in production. Of those wells not in production, most are currently in construction or are part of an asset currently under development. A minority of the smart wells have since been shut-in. An important percentage of the smart well feasibility studies indicated an improved business case with the smart well option, but were nevertheless not progressed with that option.
Projects, assets and wells from each company have been considered, specifically including a distribution of outcomes (i.e., including wells where smart completions were thought to give substantially positive and those with unknown or potentially negative benefits). The role of the simulation on the initial justification process has been reviewed, looking for both good practices and opportunities for improvement. After considering these case studies as specific examples, best practices and recommendations for process improvement and analytical tool requirements are given.
|File Size||77 KB||Number of Pages||6|