High-Resolution Reservoir Mapping: From Ultradeep Geosteering Tools to Real-Time Updating of Reservoir Models

Arata, F.; Gangemi, G.; Mele, M.; Tagliamonte, R.L.; Tarchiani, C.; Chinellato, F.; Denichou, Jean Michel; Maggs, David

doi:10.2118/183133-ms

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…In [2], it is explained that the full-field 3D model typically ignores or blends small-scale features such as faults and facies changes that can impact drilling.…”

Section: Current Methods For Geosteeringmentioning

confidence: 99%

“…Furthermore, in [12], it is discussed how the resolution of the grid cannot be modified in a flexible manner. In [27], it is emphasized that a normal procedure to reduce the complexity and size of the grid is to simplify or leave out known structural elements such as faults (as exemplified in [2]). For drilling in complex geology, where structural accuracy in the model around and ahead of bit is of particular importance, such limitations and practices are far from optimal.…”

Section: A5 Gridding Strategymentioning

confidence: 99%

“…The colors in the figure indicate property values. line that splits Φ 1 into 1 is the image of a part of the fault, correspondingly for Φ2 . Note that the direction of the arrows in the figure indicates how property values evaluated from the property functions are transferred to the subgrids.…”

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confidence: 99%

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A Novel Method for Locally Updating an Earth Model While Geosteering

Suter¹,

Cayeux²,

Friis³

et al. 2017

IJG

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Earth models are important tools for support of decision making processes for optimal exploitation of subsurface resources. For geosteering and other realtime processes where time is a major constraint, effective model management is decisive for optimal decision support. During drilling, subsurface information is received which should optimally be used to modify the 3D earth model. Today this model is typically not altered during the operation. We discuss the principles of a novel method that enables a populated earth model grid to be locally modified when the topology (connectivity) of the geological structure is locally altered. The method also allows local updates of the grid resolution. The modelled volume is split into closed regions by the structural model. Each region is individually discretized and obtains its own subgrid. Properties are stored in separate functions, e.g. for each layer, and transferred into each subgrid via a mapping. A local update of the geological structure implies that only subgrids in regions that are directly affected by the updated structure must be discarded and rebuilt, and the rest of the populated earth model grid is retained. Our focus is on decision support for optimal well placement while geosteering. The proposed method aims to manage multiple model realizations that are never fixed and always locally updated with the most recent measurements and interpretations in real-time, and where each realization is always kept at an optimal resolution.

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“…In [2], it is explained that the full-field 3D model typically ignores or blends small-scale features such as faults and facies changes that can impact drilling.…”

Section: Current Methods For Geosteeringmentioning

confidence: 99%

Section: A5 Gridding Strategymentioning

confidence: 99%

See 1 more Smart Citation

A Novel Method for Locally Updating an Earth Model While Geosteering

Suter¹,

Cayeux²,

Friis³

et al. 2017

IJG

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A Deep Learning Based Geosteering Method Assembled with "Wide-angle Eye"

Liu

Zhu

Líu

et al. 2018

Proceedings of the 2018 VII International Conference on Network, Communication and Computing

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Strategic geosteering workflow with uncertainty quantification and deep learning: Initial test on the Goliat Field data

Rammay,

Alyaev,

Larsen

et al. 2024

GEOPHYSICS

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Continuous integration of real-time logging-while-drilling data into a subsurface model with relevant geological uncertainties enables strategic geosteering: a field-level optimization of the well-placement strategy. Model errors arising from oversimplified conceptual geological models and imperfect simulation of measurements result in unreliable subsurface-model updates. The model errors are particularly pronounced when synthetic measurements are approximated with a fast but imperfect model, such as a deep neural network (DNN).#xD;We present a practical data-assimilation workflow consisting of offline and online phases. The offline phase involves DNN training and building an uncertain prior near-well geo-model. The online phase utilizes the flexible iterative ensemble smoother (FlexIES) to perform real-time assimilation of extra-deep electromagnetic data while accounting for the model errors in the approximate DNN model. We demonstrate the proposed workflow on historic well-log data from the Goliat Field (Barents Sea). #xD;The median of our probabilistic estimation is on par with proprietary inversion, regardless of the number of layers in the chosen prior or the approximate DNN model. By estimating model errors, FlexIES automatically quantifies the uncertainty in the boundaries and resistivities of layers, which is not standard in proprietary inversion. #xD;This capability allows us to capture uncertainties more efficiently, thus providing input for future quantitative decision support methods. We demonstrate the potential of quantitive decision support by visually estimating the ahead-of-bit risk of reservoir exit that has occurred during the considered operation.

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High-Resolution Reservoir Mapping: From Ultradeep Geosteering Tools to Real-Time Updating of Reservoir Models

Cited by 7 publications

References 15 publications

A Novel Method for Locally Updating an Earth Model While Geosteering

A Novel Method for Locally Updating an Earth Model While Geosteering

A Deep Learning Based Geosteering Method Assembled with "Wide-angle Eye"

Strategic geosteering workflow with uncertainty quantification and deep learning: Initial test on the Goliat Field data

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