Fast assimilation of frequently acquired 4D seismic data for reservoir history matching
Introduction
4D seismic monitoring is of significant importance for hydrocarbon reservoir surveillance and CO2 sequestration assessment. For instance, 4D seismic data has shown the strongest impact on deep-water developments in West Africa and the Gulf of Mexico (Johnston, 2013). At Sleipner field, with support from the frequently acquired 4D seismic monitors, nearly 16 million tons of CO2 has been stored to the reservoir (Xue et al., 2017). To quantitatively maximize the value of information captured by the 4D surveys, the 4D seismic data has to be assimilated to the reservoir prediction models. This data assimilation procedure is called seismic history matching (SHM), which closes the loop between the observed 4D seismic (and production) and that predicted by reservoir models (Gosselin et al., 2003; Stephen et al., 2006; Yin et al., 2017; Zhang and Leeuwenburgh, 2017). The objective is to quantitatively reduce the uncertainty surrounding reservoir management decisions, by obtaining reliable prediction of reservoir behaviours using history-matched reservoir models. It is believed that the 4D seismic adds spatial constraints to the reservoir simulation models, and thus helps to tackle the problem of non-uniqueness in the ill-posed conventional production history matching (HM).
Because of the valuable information provided by the 4D seismic, frequently repeated seismic monitoring has nowadays become more widely applied in offshore environments through the towed-streamer technology. Table 1 provides a snapshot of the fields that have five or more repeated 4D seismic surveys from the literature. To enhance the data quality and obtain seismic monitors more frequently, seabed permanent reservoir monitoring (PRM) has become popular to provide life-of-field seismic surveys. Table 2 collects a number of the major fields throughout the world that have the PRM system installed. For these fields, the permanently installed acquisition system delivers well resolvable 4D seismic data within a rapid-turn-around processing time. For example, in Ekofisk, it enables excellent 4D seismic data to be obtained every six-months (Grandi et al., 2013). The most recent progress has come from the continuous seismic monitoring technique of “SeisMovie” (Mateeva et al., 2015). With a land buried source and receiver arrays, this PRM system offers time-lapse seismic data that can image subtle reservoir changes on a daily basis. These frequently acquired surveys enable the 4D seismic data to impact the reservoir development decisions promptly regarding infill well drillings, well interventions and production optimization.
However, such frequent 4D seismic acquisition imposes new challenges to the conventional SHM workflows. First, history matching to multiple 4D seismic surveys requires the ability to handle large volumes of seismic data created by the high number of seismic surveys. It can be very computationally demanding and time-consuming if using the traditional history matching workflow that attempts to match each individual time-lapse seismic. Besides, using 4D seismic directly as history matching input data without proper analysis may lead to seismic errors being propagated systematically to the quantitative SHM workflows (Alfonzo et al., 2017). This is because as an ill-posed inverse problem, SHM is sensitive to data errors which means small errors in data the can result in large fluctuations in the prediction (Li, 2017). This problem can further propagate with the increased number of 4D seismic surveys.
In this paper, we propose a framework to efficiently assimilate the frequently acquired 4D seismic data in SHM. A new 4D seismic attribute (named “well2seis”) for history matching is introduced that condenses the many repeated 4D seismic data into a single unitless attribute by correlating them to the reservoir production performances. This not only compresses the big volumes of frequently acquired 4D seismic into a single attribute for efficient assimilation, but also reduces the uncertainty of the 4D seismic observations by summarizing them based on production performances. Morris sensitivity analysis is adapted to investigate the uncertainty parameters in the reservoir model. Once the reservoir uncertainty parameters are confirmed, a well2seis objective function (OF) is constructed to quantify the misfit between the observed well2seis (calculated using observed 4D seismic and production data) and modelled well2seis (calculated using modelled 4D seismic and production performances). In the conventional SHM, because production and seismic data are in different metrics (Chassagne et al., 2016), the weights on seismic and production misfits have to be properly specified when combining them into the OF. But this is avoided when using well2seis OF, as it uses only the well2seis to calculate the misfit, while summarizing information from both 4D seismic and production data. ES-MDA is applied at the end to minimize the well2seis OF by calibrating the uncertainty model parameters identified by the Morris sensitivity analysis. This proposed workflow is tested on a North Sea field reservoir and compared to the conventional production and seismic history matching practices.
Section snippets
Condensing the frequently repeated 4D seismic data
All the reservoir-induced dynamic changes detected by 4D seismic data are caused by fluid extraction or injection activity from the wells. 4D seismic signals therefore cannot be unambiguously interpreted without a clear understanding of the field production and injection behaviours. Considering a reservoir with n repeated time-lapse seismic surveys acquired during the development, a total of N = n*(n-1)/2 4D seismic differences can be generated for all paired combinations of surveys. They will
A North Sea reservoir case
To demonstrate the application of the above proposed data assimilation workflow, we applied it to a reservoir model derived from a real North Sea field with frequently repeated 4D seismic acquisitions. With the geological features and development history kept unchanged as the real field, this reservoir model is generalized to be a prototype reservoir case to investigate the data assimilation problems of history matching. We create a “Truth” case by picking a best history matched reservoir model
Discussions
Our study further develops a cross-disciplinary dimensionless attribute uniting the frequently repeated 4D seismic surveys with the production data to calibrate reservoir models and reduce prediction uncertainty. This well2seis attribute for data assimilation first condenses all the available 4D seismic from multiple surveys into a single attribute, such that it avoids directly history matching to large volumes of seismic data created from the many repeated surveys. This improves the previous
Conclusions
This paper presents a scheme for fast assimilating of frequently repeated 4D seismic surveys that normally contains large volume of measurement data. Instead of using the conventional data assimilation methods that directly history match to the reservoir production history and 4D seismic data, the 4D seismic and production data are first correlated to generate a dimensionless cross-disciplinary attribute, which compresses the information from both the production history and many repeated 4D
Authorship statement
First author: proposed the main workflow, conducted the technical development, drafted and revised this paper. Second author: provided critical insights by implementing the proposed workflow to real field assets, and assisted drafting of the manuscript. Third author: supervised the research project and provided critical insights in drafting and revising the manuscript.
Computer code availability
The code and manual of the Ensemble Reservoir Tool (ERT) used for this research can be obtained freely from: http://ert.nr.no/ert/index.php/The_ERT_code.
Acknowledgments
We thank Equinor for sponsoring this research project. We also want to thank the sponsors of the Edinburgh Time Lapse Project Phase VI (Aker BP, BG, BP, CGG, Chevron, ConocoPhillip, ENI, Equinor, ExxonMobil, Halliburton, Hess, Ikon Science, Maersk, Nexen, Norsar, OMV, Petrobras, RSI, Shell, and TAQA) for supporting this research. The authors would like to specially thank Milana Ayzenberg, Rachares Petvipusit, Matteo Ravasi, Romain Chassagne, Hamed Amini and Mingyi Wong for the technical
References (49)
- et al.
A global sensitivity analysis of two-phase flow between fractured crystalline rock and bentonite with application to spent nuclear fuel disposal
J. Contam. Hydrol.
(2015) - et al.
Ensemble smoother with multiple data assimilation
Comput. Geosci.
(2013) Analysis of the performance of ensemble-based assimilation of production and seismic data
J. Pet. Sci. Eng.
(2016)- et al.
Morris method of sensitivity analysis applied to assess the importance of input variables on urban water supply yield – a case study
J. Hydrol.
(2013) - et al.
Global sensitivity analysis of an end-to-end marine ecosystem model of the North Sea: factors affecting the biomass of fish and benthos
Ecol. Model.
(2014) - et al.
Multi-method global sensitivity analysis of flood inundation models
Adv. Water Resour.
(2008) - et al.
Research and development of a permanent OBC system for time-lapse seismic survey and microseismic monitoring at the offshore CO2 storage sites
Energy Procedia
(2017) - et al.
Evaluation of inter-well connectivity using well fluctuations and 4D seismic data
J. Pet. Sci. Eng.
(2016) - et al.
Utilization of time-lapse seismic for reservoir model conditioning
- et al.
Analysis and calibration of 4D seismic data prior to 4D seismic inversion and history matching - norne field case
A Pragmatic Approach to Simulator to Seismic Modelling for 4D Seismic Interpretation
Accessing a North Sea reservoir connectivity from 4D seismic and production data
4D seismic monitoring of water influx at bay marchand: the practical use of 4D in an imperfect world
SPE Reservoir Eval. Eng.
Seismic permanent reservoir monitoring (PRM)–A growing market
First Break
Optimising value through improved 4D seismic processing on 10 vintages-foinaven-schiehallion-loyal case history
An effective screening design for sensitivity analysis of large models
Environ. Model. Softw
An analysis of the seismic history matching objective function
One-at-a-Time plans
J. Am. Stat. Assoc.
History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations
Comput. Geosci.
Deterministic ensemble smoother with multiple data assimilation as an alternative for history-matching seismic data
Comput. Geosci.
Seabed permanent reservoir monitoring (PRM) – a valid 4D seismic technology for fields in the North Sea
First Break
Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics
J. Geophys. Res.: Oceans
Seismic history matching using a fast-track simulator to seismic proxy
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Now at: Department of Geological Sciences, Stanford University, CA, 94305.