Evaluation of inter-well connectivity using well fluctuations and 4D seismic data

Yin, Zhen Yu; MacBeth, Colin; Chassagne, Romain; Vazquez, Oscar

doi:10.1016/j.petrol.2016.06.021

Cited by 25 publications

(11 citation statements)

References 22 publications

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“…Even though CRMs were initially developed for waterflooding [7][8][9], models and field applications for primary [10,11] and tertiary recovery [12][13][14][15] have also been developed over recent years. It is important to emphasize that these models do not intend to capture all of the reservoir physics but are expected to identify the primary drivers, and provide a reasonably accurate basis for analysis and forecasts.…”

Section: A) B)mentioning

confidence: 99%

“…The use of CRMP considering both total and oil production instilled confidence in solutions for this pattern, as Figure 17 demonstrates. For additional tertiary recovery field applications, the reader is referred to [13,15,47,81].…”

Section: Tertiary Recoverymentioning

confidence: 99%

“…In the cases of gas injection, the total compressibility is a function of reservoir pressure and saturation, which possibly causes significant variations in time constants (Equation (4)) and productivity indices during the history matching and forecasting windows. However, some field applications did not present a specific formulation to address this issue during gas injection [12,15,81], and still obtained satisfactory results for the analysis performed, suggesting that, in some cases, it may not be necessary to consider the time-varying behavior of the parameters. For example, in supercritical conditions, carbon dioxide behaves like a liquid [14].…”

Section: Gas Content Of Reservoir Fluidsmentioning

confidence: 99%

“…Yin et al [15] integrated 4D seismic-based results with CRM evaluations to delineate faults and large-scale conduits and detected a sub-seismic fault, which assisted the history matching of a finite-difference reservoir model for the Norne field (Norway).…”

mentioning

confidence: 99%

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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: A) B)mentioning

confidence: 99%

Section: Tertiary Recoverymentioning

confidence: 99%

Section: Gas Content Of Reservoir Fluidsmentioning

confidence: 99%

mentioning

confidence: 99%

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

et al. 2018

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“…Direct methods are based on a test performed in a reservoir such as tracer testing, multiple well testing, and 4D seismic surveying . Although direct methods are accurate, they are expensive and time‐consuming.…”

Section: Introductionmentioning

confidence: 99%

On determination of interwell connectivity under immiscible gas injection process: Modified capacitance‐resistance model

et al. 2018

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Interwell connectivity is an important parameter in reservoir management and optimization during water/gas injection. As an analytical approach, the capacitance-resistance model (CRM) is a rapid tool that only needs some common and available data for field performance prediction. The nonlinear signal processing technique is used with the CRM to determine reservoir continuity between production and injection wells. Current CRMs are applicable in water flooding systems. In this study, a modified capacitance-resistance model (M-CRM) for interwell connectivity calculation in immiscible gas flooding projects is developed based on the mass balance equation. Slightly compressible flow is one of the main assumptions in CRM development while gas is compressible and the gas property variation with pressure should be scrutinized in the equations. Therefore, new equations need gas PVT properties to consider the effect of gas compressibility. Moreover, the productivity equation for oil and gas production should be revised. The constructed model that considers mass balance and the productivity equation is applied in two synthetic models and one real sector reservoir model. The genetic algorithm, as an optimization tool for solving the new model, is used and streamline simulation is selected as a validation tool for interwell connectivity parameter calculation. Based on the streamline results, it was observed that a M-CRM is able to predict the reservoir behaviour better than a common CRM for pre/post breakthrough conditions in gas injection scenarios. Also, an analysis is performed for different parameters that affect the new model in immiscible gas flooding. Results show that modification on the mass balance equation has a greater influence than the productivity equation.

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Value of information of time-lapse seismic data by simulation-regression: comparison with double-loop Monte Carlo

Dutta

Mukerji

Eidsvik³

2019

Comput Geosci

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Simulation-regression is a computationally efficient methodology to estimate the value of information (VOI), as it involves directly estimating the value outcomes corresponding to different data realizations by building a statistical relationship between the prospect values and the data, rather than estimating the model parameters from the data and then estimating the value outcomes given the model parameters. The simulation-regression workflow is applied to estimate the VOI of time-lapse seismic data in a 2D reservoir case using Partial Least Squares Regression (PLSR) and Principal Components Regression (PCR), and the variance in the VOI result is estimated using bootstrapping for varying number of realizations. The VOI results from the two regression techniques are found to be consistent, and it is seen from the bootstrap results that the variance in the VOI decreases with increasing number of realizations and the VOI ranges obtained by higher number of realizations are captured by those obtained by fewer realizations. The VOI results from simulation-regression are then compared with those obtained by a rigorous Monte Carlo method, where the posterior model realizations are sampled using rejection sampling for each possible data realization, and then the prospect values are estimated for each model realization using flow simulation. Finally, the simulation-regression method is applied to estimate the VOI of timelapse seismic data in a complex production optimization case involving sequential well placement and control decisions.

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Evaluation of inter-well connectivity using well fluctuations and 4D seismic data

Cited by 25 publications

References 22 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

On determination of interwell connectivity under immiscible gas injection process: Modified capacitance‐resistance model

Value of information of time-lapse seismic data by simulation-regression: comparison with double-loop Monte Carlo

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