Seismic assisted history matching using binary maps

Obidegwu, Dennis Chinedu; Chassagne, Romain; MacBeth, Colin

doi:10.1016/j.jngse.2017.03.001

Cited by 27 publications

(6 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, since this initial model is highly uncertain, the production data are used to mitigate the model uncertainties. History matching is widely used in the oil and gas industry, and many different methods of performing it have been published in the literature (Chakra and Saraf 2016;Hamdi et al 2015;Makhlouf et al 1993;Obidegwu et al 2017;Oliver and Chen 2011;Oliver et al 2008;Tunnish et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Effect of systematic and random flow measurement errors on history matching: a case study on oil and wet gas reservoirs

Sadri

Shariatipour

Hunt³

et al. 2019

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

History matching is the process of modifying a numerical model (representing a reservoir) in the light of observed production data. In the oil and gas industry, production data are employed during a history matching exercise in order to reduce the uncertainty in associated reservoir models. However, production data, normally measured using commercial flowmeters that may or may not be accurate depending on factors such as maintenance schedules, or estimated using mathematical equations, inevitably has inherent errors. In other words, the data which are used to reduce the uncertainty of the model may have considerable uncertainty in itself. This problem is exacerbated for gas condensate and wet gas reservoirs as there are even greater errors associated with measuring small fractions of liquid. The influence of this uncertainty in the production data on history matching has not been addressed in the literature so far. In this paper, the effect of systematic and random flow measurement errors on history matching is investigated. Initially, 14 production data sets with different ranges of systematic and random errors, from 0 to 10%, have been employed in a history matching exercise for an oil reservoir and the results have later been evaluated based on a reference model. Subsequently, 23 data sets with errors ranging from 0 to 20% have been employed in the same process for a wet gas reservoir. The results show that for both cases systematic errors considerably affect history matching, while the effect of random errors on the considered scenarios is seen to be insignificant. Although reservoir model parameters in the wet gas reservoir were not as sensitive to the flow measurement errors as in the oil reservoir, for both cases, the future production forecast was significantly affected by the errors. Permeability was seen to be the most sensitive history matching parameter to the flow measurement errors in the oil reservoir, while for the wet gas reservoir, the most sensitive parameter was the forecast of future oil and gas production. Finally, considering the noticeable effect of systematic errors on both cases, it is suggested that flowmeter calibration and regular maintenance is prioritised, although the subsequent economic cost needs to be considered.

show abstract

Section: Introductionmentioning

confidence: 99%

Effect of systematic and random flow measurement errors on history matching: a case study on oil and wet gas reservoirs

Sadri

Shariatipour

Hunt³

et al. 2019

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

show abstract

“…They highlight the inability of the reservoir simulation to properly describe the flow of gas across the reservoir. This may hint at possible updates to the reservoir model in properties such as critical gas saturation and permeability (Obidegwu et al., 2017) or a possible need to refine the spatial resolution of the reservoir model to achieve a more precise simulation of gas fluid flow. The global distribution of gas also provides insights into the reservoir connectivity.…”

Section: Discussionmentioning

confidence: 99%

Bayesian inversion of 4D seismic data to pressure and saturation changes: Application to a west of Shetlands field

Côrte

Amini

MacBeth

2022

Geophysical Prospecting

Self Cite

View full text Add to dashboard Cite

A Bayesian inversion methodology is proposed that inverts angle-stacked 4D seismic maps to changes in pressure, water saturation and gas saturation. The inversion method is applied to data from a siliciclastic reservoir in the west of Shetlands, UK continental shelf. We present inversion results for three seismic monitor surveys and demonstrate the added value of pressure-saturation inversion by providing insights into reservoir connectivity and fluid dynamics across 14 years of reservoir production. In these surveys, 4D seismic signals related to waterflood, pressure increase and depletion, and gas exsolution are evident and overlap each other in many regions. To regularize this ill-posed inversion problem, we propose a Bayesian formulation that incorporates spatially variant prior information derived from a history-matched reservoir simulation model and well pressure measurements. The benefit of incorporating these multi-disciplinary data as prior information is demonstrated by comparing to inversion results using a spatially invariant prior. We show that the method takes advantage of the multi-disciplinary prior information to make more precise inferences where the seismic data are most uncertain. This leads to more realistic spatial distributions for the pressure and water-saturation inversion results. The non-uniqueness in this non-linear inversion is studied by analysing uncertainty estimates produced by stochastic sampling of the Bayesian posterior distribution. Posterior standard deviations are observed to be related to the sensitivity of the seismic amplitudes to the changes in each dynamic property as well as the degree of overlap between changes in different dynamic properties. Estimated pressure increases have a posterior standard deviation of approximately 1 MPa, whereas posterior standard deviations for pressure decrease are on average 4 MPa, with higher values applicable to regions of gas exsolution. The posterior standard deviation for estimates of gas saturation change is 0.07 for moderate, visible saturation signals, but 0.005 for low-to-zero gas saturation change. The posterior standard deviation for estimated water saturation change is mainly influenced by overlapping changes in pressure and gas saturation. When water changes dominate the 4D seismic signal, posterior standard deviations are on average 0.05. These values rise to 0.25 in areas where the water change is obscured by pressure or gas-saturation changes.

show abstract

“…With substantially larger spatial coverages, 4D seismic data provide valuable additional information that is complementary to that from the conventional core sample, well log and production data. For this reason, in the past two decades, there is increasing interest in incorporating 4D seismic data for quantitative reservoir characterization through computer-assisted history matching, for example, see Abadpour et al (2013); Dadashpour et al (2008); Emerick (2016); Gosselin et al (2003); Ketineni et al (2018); Lorentzen et al (2019); Luo et al ( , 2018a; Obidegwu et al (2017); Soares et al, 2020;Taha et al (2019); Trani et al (2012); Zhang and Leeuwenburgh (2017); Skjervheim et al (2007).…”

Section: The 4d Seismic History Matching Frameworkmentioning

confidence: 99%

Accounting for model errors of rock physics models in 4D seismic history matching problems: A perspective of machine learning

Luo¹,

Lorentzen²,

Bhakta³

2021

Preprint

View full text Add to dashboard Cite

Model errors are ubiquitous in practical history matching problems. A common approach in the literature to accounting for model errors is to treat them as random variables following certain presumed distributions. While such a treatment renders algorithmic convenience, its underpinning assumptions are often invalid. In this work, we adopt an alternative approach, and treat model-error characterization as a functional approximation problem, which can be solved using a generic machine learning method. We then integrate the proposed model-error characterization approach into an ensemble-based history matching framework, and show that, with very minor modifications, existing ensemble-based history matching algorithms can be readily deployed to solve the history matching problem in the presence of model errors.To demonstrate the efficacy of the integrated history matching framework, we apply it to account for potential model errors of a rock physics model in 4D seismic history matching applied to the full Norne benchmark case. The numerical results indicate that the proposed model-error characterization approach helps improve the qualities of estimated reservoir models, and leads to more accurate forecasts of production data. This suggests that accounting for model errors from a perspective of machine learning serves as a viable way to deal with model imperfection in practical history matching problems.

show abstract

Seismic assisted history matching using binary maps

Cited by 27 publications

References 42 publications

Effect of systematic and random flow measurement errors on history matching: a case study on oil and wet gas reservoirs

Effect of systematic and random flow measurement errors on history matching: a case study on oil and wet gas reservoirs

Bayesian inversion of 4D seismic data to pressure and saturation changes: Application to a west of Shetlands field

Accounting for model errors of rock physics models in 4D seismic history matching problems: A perspective of machine learning

Contact Info

Product

Resources

About