4D seismic history matching

Oliver, Dean S.; Fossum, Kristian; Bhakta, Tuhin; Sandø, Ivar; Nævdal, Gunnar; Lorentzen, Rolf Johan

doi:10.1016/j.petrol.2021.109119

Cited by 41 publications

(18 citation statements)

References 146 publications

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“…When performing an assisted history matching using seismic data, we can integrate the seismic data in various levels, i.e. (i) the seismic level, (ii) the elastic parameter level and (iii) the simulation model level [25]. The amount of work for either on forward modeling or on real data preparation depends on the level of data integration.…”

Section: Seismic Data Typesmentioning

confidence: 99%

“…For example, 2D map based attribute is the most robust attribute to select, though, it doesn't contain any vertical information. On the other hand, 3D attribute contains vertical information, however might have more challenges in up-scaling, interpolating between reservoir and seismic domain [25].…”

Section: Approach To Integrate Seismic Datamentioning

confidence: 99%

“…Here, an important question is what level of simulated differences are required to produce a significant 4D effect, and whether the model tends to over-predict or under-predict 4D effects. By checking whether the remaining discrepancies between models and data are within the range explained by model parameter variations, we can decide whether to accept or reject the model instead of performing the full history matching and conclude about the inadequacy of the model [25].…”

Section: Approach To Integrate Seismic Datamentioning

confidence: 99%

“…Although history matching of production data is a common standard in the industry, and there exist a large amount of 4D seismic data, utilizing the 4D seismic data for history matching is not considered as a standard workflow [25]. The 4D seismic data is mostly used for interpretation to find regions of the reservoir where changes in the pressure or fluid content have occurred.…”

Section: Introductionmentioning

confidence: 99%

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Workflow for adding 4D seismic data in history matching

Bhakta,

Haukås,

Lorentzen

et al. 2021

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In this document we present a workflow for ensemble-based 4D seismic history matching. Ensemble-based history matching has become standard for production data, but 4D seismic data poses a number of additional challenges. One issue is that the amount of data is considerably larger, but another, probably more complicating factor is that for utilizing the seismic data, either the seismic data must be inverted to properties that is included in the reservoir simulation model, or a seismic response must be modeled, given the current estimate of the reservoir properties. This leads to a number of choices on how to utilize the information of the 4D seismic data. We will discuss this, as well as point to approaches for handling large amounts of data in ensemble-based history matching. The developed approach has been applied on the Norne field and is currently being evaluated at the Ekofisk field. This document is primarily addressed to reservoir engineers and researchers that are working on history matching 4D seismic data, but it might also be of interest to those working with 4D seismic data from a geophysical perspective. After all, 4D seismic history matching should be viewed as an interdisciplinary subject. Although, our focus has been on ensemble-based history matching, some of the choices that have to be made in utilizing 4D seismic data is independent of the actual method used for history matching.

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Section: Seismic Data Typesmentioning

confidence: 99%

Section: Approach To Integrate Seismic Datamentioning

confidence: 99%

Section: Approach To Integrate Seismic Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Workflow for adding 4D seismic data in history matching

Bhakta,

Haukås,

Lorentzen

et al. 2021

Self Cite

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“…AVO and AVA inver-for reservoir characterization (Tarantola, 2005). Oliver et al (2021) provide a thorough review of how AVA is the most promising of geophysical techniques for predicting petrophysical properties of the reservoir. However, it is also the noisiest.…”

Section: Introductionmentioning

confidence: 99%

Naïve Bayesian classification of cumulative oil production from stochastic amplitude variation with angle inversion attributes: As applied to SEAM and a West Africa Field

Hayden

Whitney

Michael

2022

Geophysical Prospecting

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Cumulative oil predictions are made from stochastically inverted earth attributes. The inverted attributes are from the SEAM Life of Field model and an offshore field in West Africa. To perform the prediction, we use a naïve Bayesian classifier for its transparency in methodology, computational efficiency and flexibility. The inverted properties we use for classification consist of porosity, P-S wave velocity ratio (Vp/Vs), acoustic impedance and density which are extracted from within a set radius around the well-path. The production from the wells in both SEAM and the West African field serve as the labels for the naïve Bayesian classifier, which are ultimately placed into two classes: the 'high' and 'low' producers. For calculating the accuracy of the classifier, we perform full cross-validation with a set number of training wells. The cross-validation accuracy is 78.0% and 83.3% for the SEAM model and the West African Field respectively. The classifier's sensitivity to the chosen radius around the wells and its inherent independence assumption are investigated and shown to change the bulk accuracy by less than 3.0%. We implement full reservoir classifications on SEAM to highlight potential drilling locations and show the capability of the classifier. Overall, we demonstrate how the naïve Bayesian classifier can efficiently and transparently synthesize the entire distribution of stochastic inversions of seismic data to predict oil production.

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