Rapid Model Updating with Right-Time Data—Ensuring Models Remain Evergreen for Improved Reservoir Management

Webb, Stephen John; Revus, David; Myhre, Angela; Goodwin, Nigel; Dunlop, Neil

doi:10.2118/112246-ms

Cited by 19 publications

(5 citation statements)

References 40 publications

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“…Uncertainty quantification using reservoir simulation has increasingly gained attention in theory and practice for making field development decisions (Webb et al, 2008;Oliver and Chen, 2011;Bazargan et al, 2013;Goodwin, 2015). For brown fields with significant production history, an ensemble of history-matched reservoir models that are constrained by observed production data is required to properly quantify the uncertainty of production forecast.…”

Section: Numerical Simulation Workflowmentioning

confidence: 99%

Comparison of Numerical vs Analytical Models for EUR Calculation and Optimization in Unconventional Reservoirs

Moinfar¹,

Erdle²,

Patel³

2016

SPE Low Perm Symposium

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Analytical models available in Rate-Transient-Analysis (RTA) packages are widely used as tools for history matching and forecasting production in unconventional resources. There has also been an increasing interest in the use of numerical simulation of unconventional reservoirs. In this study, we use both methods to history match the production of hydraulically fractured unconventional wells, followed by forecasting future production to establish a well's EUR (Estimated Ultimate Recovery) for reserves determination purposes. This study's goal is to quantify the differences one might expect to encounter in a well's EUR when using analytical model-based RTA vs numerical simulation-based workflows in unconventional reservoirs.First, we consider an undersaturated shale oil reservoir as a base model for this study. The base case also satisfies all assumptions inherent to analytical solution-based methods such as homogenous reservoir properties and fully-penetrating planar fractures. An excellent match between results of both methods for the base model validates the numerical simulation approach. We then impose a series of real-world deviations from RTA assumptions and investigate reliability of EUR predictions made by both approaches. In all cases, historical data and reference EURs are derived from finely-gridded numerical simulations.Example results show that, in the presence of real-world deviations from RTA assumptions, analytical models can still match the historical production data; however, key reservoir and fracture parameters need to be modified drastically to compensate for the lack of sufficient physics in the analytical models. Results show that the analytical solution-based history-matched models are not predictive for future production, and somewhat surprisingly provide pessimistic EURs in all real-world scenarios investigated in this work. For the cases presented in this study, analytical models under-predict EURs by 6-17% when two years of production history is available for matching. The underestimation of EUR increases drastically (up to 60%) as the length of available historical data decreases from 2 years to 3 months.For all cases, we also apply an efficient numerical simulation-based workflow for probabilistic forecasting of EURs. This workflow provides multiple history-matched models that are constrained by historical production data. The probabilistic forecast method employed in this work provides P90 (conservative), P50 (most likely), and P10 (optimistic) values for EUR. In all examples, the range of P90 to P10 EUR values includes the reference EUR, and the P50 values are within 2.2% of the reference EUR.

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Section: Numerical Simulation Workflowmentioning

confidence: 99%

Comparison of Numerical vs Analytical Models for EUR Calculation and Optimization in Unconventional Reservoirs

Moinfar¹,

Erdle²,

Patel³

2016

SPE Low Perm Symposium

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“…Alternatively, we can remove some history, do a prediction study, and evaluate how well the prediction followed the history -or, better, whether the history falls within the range of prediction. (Webb, et al, 2008) However, there are many other factors which have an effect, particularly the prior beliefs embodied in the reservoir models, and the future operational decisions compared with the prior assumptions.…”

Section: G) Testing Against What Actually Happensmentioning

confidence: 99%

Bridging the Gap Between Deterministic and Probabilistic Uncertainty Quantification Using Advanced Proxy Based Methods

Goodwin

2015

SPE Reservoir Simulation Symposium

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The use of proxy models for optimisation of expensive functions has demonstrated its value since the 1990's in many industries. Within reservoir engineering, similar techniques have been used for over a decade for history matching, both in commercial tools and in-house software.In addition to efficient history matching, proxy models have a distinct advantage when performing uncertainty quantification of probabilistic forecasts. Markov Chain Monte Carlo (MCMC) methods cannot realistically be applied directly with reservoir simulations, and even fast proxy models can fail dramatically to adequately represent the range of uncertainty if implemented without due care.A pitfall of the use of proxy models is that they are considered 'black box' and their quality is difficult to measure. Engineers prefer to deal with deterministic simulation models which they can evaluate and understand.The main pitfall of simple random walk MCMC techniques, which have begun to appear within reservoir engineering workflows, is a focus on theoretical properties which are not observed in practical implementations. This gives rise to potential gross errors, which are not generally appreciated by practitioners. Advances in recent years within the field of Bayesian statistics have significantly improved this situation, but have not yet been disseminated within the oil and gas industry. This paper describes the limitations of random walk MCMC techniques which are currently used for reservoir prediction studies, and shows how Hamiltonian MCMC techniques, together with an efficient implementation of proxy models, can lead to a more reliable and validated probabilistic uncertainty quantification, whilst also generating a suitable ensemble of deterministic reservoir models. Scientific comparison studies are performed for both an analytical case and a realistic reservoir simulation case to demonstrate the validity of the approach.The benefit of this methodology is to allow asset teams to effectively manage reservoir decisions using a robust and validated understanding of uncertainty. It lays the scientific foundations for the next generation of uncertainty tools and workflows.

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“…Reservoir engineers are now better equipped to explore multiple scenarios for dynamically modeling reservoirs and depletion plans. The automated and iterative nature of these applications emphasizes the importance of accurate and quality-controlled data more than ever, since optimizations rely on better behaving data (Webb et al, 2008). As a result of the deficiencies in existing tools and increased understanding of the value of real time data, there was considerable interest among reservoir engineers in an application to seamlessly bring historical data to reservoir models in a user-friendly manner in order to improve productivity.…”

Section: Reservoir Model Update Processmentioning

confidence: 99%

Continuous Improvement Through Real-Time Data Integration into Reservoir Management Workflows

Kragas¹,

Gokdemir²

2010

Proceedings of SPE Intelligent Energy Conference and Exhibition

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A significant challenge facing companies implementing digital oilfield technologies is integration. Instrumentation delivers vast quantities of high frequency downhole and surface real-time data over the life of a well. Integrating this data into engineering applications and workflows to capture its value is often a manual, time-intensive process. This is particularly true when data needs to cross functional boundaries or disciplines, such as integrating production data gathered by real-time systems into reservoir simulation and history matching processes. Currently, field-specific or even engineer-specific processes lead to inefficient use of real-time data.We describe the development and implementation within BP's Field of the Future Program of a software application for automatically transforming real-time production data into a simulation-ready format for reservoir management. The web-based application allows access to multiple, different databases and data files that store measured or derived dynamic subsurface data. It provides a sustainable, standardized process by streamlining the most common activities of updating simulation models, including data access, retrieval, merging, transformations, quality control, and formatting. The application is built on principles of serviceoriented architecture and component-based, plug-in technologies, providing well-defined boundaries and interfaces between application components and extensibility for adding new simulation data sources, formats, and workflows.The application has been implemented for nine BP assets in 2009. It has been used by reservoir engineers to increase the update frequency of simulation models with production data, and has reduced the time required for updates to minutes compared to days in previous manual processes. Expansions to functionality and automation, and further at-scale deployments are ongoing.By automating data access, retrieval, quality control, and formatting, the time required to update simulation models with historical data is greatly reduced, giving reservoir engineers more time to focus on critical engineering tasks. The shortened time for data preparation enables more frequent model updates, mitigating risks associated with decisions based on outdated reservoir model predictions. In addition, the application provides a consistent, sustainable process for model updating and data quality assurance. Typical benefits of a standardized tool are also realized, including reduced training requirements and access to a defined workflow.

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Rapid Model Updating with Right-Time Data—Ensuring Models Remain Evergreen for Improved Reservoir Management

Cited by 19 publications

References 40 publications

Comparison of Numerical vs Analytical Models for EUR Calculation and Optimization in Unconventional Reservoirs

Comparison of Numerical vs Analytical Models for EUR Calculation and Optimization in Unconventional Reservoirs

Bridging the Gap Between Deterministic and Probabilistic Uncertainty Quantification Using Advanced Proxy Based Methods

Continuous Improvement Through Real-Time Data Integration into Reservoir Management Workflows

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