A Data-Driven Approach Enhances Conventional Reservoir Surveillance Methods for Waterflood Performance Management in the North Sea

Jahangiri, Hamid Reza; Adler, Carwyn; Shirzadi, Shawn; Bailey, R. S.; Ziegel, Eric R.; Chesher, James; White, Matthew

doi:10.2118/167849-ms

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…Figure 14b shows the declining oil rate during the post-breakthough period, suggesting that the relative-permeability effects triggered the decline in the productivity index, J, from 16 to 8 STB day•psi . For additional field applications in evolving waterflooding, the reader is referred to [44,77,104].…”

Section: Evolving Waterfloodmentioning

confidence: 99%

A State-of-the-Art Literature Review on Capacitance Resistance Models for Reservoir Characterization and Performance Forecasting

et al. 2018

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Capacitance resistance models (CRMs) comprise a family of material balance reservoir models that have been applied to primary, secondary and tertiary recovery processes. CRMs predict well flow rates based solely on previously observed production and injection rates, and producers’ bottomhole pressures (BHPs); i.e., a geological model and rock/fluid properties are not required. CRMs can accelerate the learning curve of the geological analysis by providing interwell connectivity maps to corroborate features such as sealing faults and channels, as well as diagnostic plots to determine sweep efficiency and reservoir compartmentalization. Additionally, it is possible to compute oil and water rates by coupling a fractional flow model to CRMs which enables, for example, optimization of injected fluids allocation in mature fields. This literature review covers the spectrum of the CRM theory and conventional reservoir field applications, critically discussing their advantages and limitations, and recommending potential improvements. This review is timely because over the last decade there has been a significant increase in the number of publications in this subject; however, a paper dedicated to summarize them has not yet been presented.

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Section: Evolving Waterfloodmentioning

confidence: 99%

A State-of-the-Art Literature Review on Capacitance Resistance Models for Reservoir Characterization and Performance Forecasting

et al. 2018

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From Market Basket Analysis to Well-head Monitoring: Using Events to Increase Oil Recovery

Bailey¹,

Lü²,

Shirzadi³

et al. 2014

All Days

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BP has a successful, limited scope deployment of a non-parametric capability called Event Detection and Association (EDA). The technology resembles ‘Market Basket Analysis’ tools used to carry out associative assessments of item sets. Variations from the original analytical approach have been found necessary before the EDA techniques could be used to solve real-world oil and gas problems. These changes are based on fundamental physical laws such as conservation of mass and the recognition that systems of relevance all contain time delays between the ‘items’ that are to be tested for association. EDA comprises part of a suite of tools collectively referred to as Top Down Waterflood Diagnostic and Optimisation (TDWF). TDWF is a top down reservoir simulation toolkit capable of developing a statistically valid reservoir representation based purely on operational data together with a limited set of configuration data (the x, y location of wells is required). EDA contributes an independent validation of the Capacitance Resistivity Model (CRM) using the same data further processed to extract causal events on the injectors and delayed-response events on the production wells. By studying the time based correlation of these input and output events the basic TDWF CRM model is either confirmed or has suggestions made for improving its predictive capability. Early studies on three reservoirs, when combined with insights from suppliers of four different data-driven technologies, confirmed the robustness of EDA. A limited trial deployment followed in four operating regions. The users’ studies have generated insights and improved the product design and capabilities. EDA can deliver insights into well connectivity in the subsurface. Working from data alone EDA can even approximate the findings of a much more expensive tracer study and deliver these insights in a very short time and at a fraction of the expense.

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Data-Driven Optimization of Injection/Production in Waterflood Operations

Temizel¹,

Nabizadeh

Kadkhodaei

et al. 2017

All Days

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Decision making in waterflooding operations is a crucial process in petroleum oilfield activities where numerous attributes and uncertainties exist in the complete process. This study investigates the reservoir management of waterfloods in terms of injection/production practices. A well-organized historical database that also collects real-time data is especially important in utilization of data-driven methods in the process of determination of optimum injection/production practices for waterfloods that will result in better recovery and sweep, which is illustrated in this paper. Statistics is a strong tool to turn information or data into knowledge when used with care and physical understanding of the cause-effect relation between attributes and the outcome. Unfortunately, historical data and learnings from the past cannot be used in an efficient way in oilfield decisions due to the lack of systematically organized historical data where there is a huge potential of turning terrabytes of data into knowledge and understanding for improved decisions and results. Historical injection and production data at pattern level is utilized to determine the optimum injection levels in light of significant factors that affect the success of a waterflooding displacement process with commercial data analysis tools. Analysis of injection/production data at associated injectors and producers reveals the optimum injection levels depending on the significant factors including but not limited to subsurface conformance, number and location of producers, vintage of wells, completion practices and injection history. The optimum injection levels change depending on the changing variables that affect the displacement and injection processes, thus, a real-time data flow from producers and injectors is required to capture and maintain the optimum operating levels. The significance of each parameter in this process is obtained in a dynamic manner with real-time feed of field data and efficiently used to determine the optimum levels of injection at a specified time. Change of important factors in the process in time is also important by means of adding another dimension on the relative significance of parameters in the process, thereby shedding light on future decisions.

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A Data-Driven Approach Enhances Conventional Reservoir Surveillance Methods for Waterflood Performance Management in the North Sea

Cited by 3 publications

References 4 publications

A State-of-the-Art Literature Review on Capacitance Resistance Models for Reservoir Characterization and Performance Forecasting

A State-of-the-Art Literature Review on Capacitance Resistance Models for Reservoir Characterization and Performance Forecasting

From Market Basket Analysis to Well-head Monitoring: Using Events to Increase Oil Recovery

Data-Driven Optimization of Injection/Production in Waterflood Operations

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