Quantification of Depth Accuracy

Brööks, Andrëw G.; Wilson, Harry; Jamieson, Angus L.; McRobbie, David; Holehouse, Steven

doi:10.2523/95611-ms

Cited by 7 publications

(1 citation statement)

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“…It it known that the vertical positioning error of long horizontal wells accumulates systematically along surveys (Brooks et al , 2005). Let E t be the difference between the true, unknown TVD and the Petroleum Geostatistics Biarritz, France, 7-11 September 2015 measured TVD at location t = MD.…”

Section: Well Tvd Uncertainty Modelmentioning

confidence: 99%

Simultaneous Prediction of Geological Surfaces and Well Paths

Dahle¹,

Almendral-Vasquez²,

Abrahamsen³

2015

Proceedings

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We present a novel model for the true vertical depth (TVD) positioning error in horizontal wells. The model utilizes a non-stationary Gaussian process known as the integrated Ornstein-Uhlenbeck process. This process is continuous and exhibits the known systematic accumulation of vertical errors with increasing measured depth. The well corrections produced are smooth and maintain the underlying shape of the well path. The smoothness can be adjusted through a correlation range parameter. Using the geological constraints contained in the zonation, we can predict or simulate surface depths and well depths simultaneously. The size of the surface and well displacements are governed by the relative magnitude of the surface and well path TVD uncertainties. The resulting well paths and surfaces stay within their uncertainty envelopes and are consistent with the zonation. The surface/well relationships can be expressed as a highly dimensional truncated multivariate Gaussian distribution. We draw samples from this distribution using an efficient rejection sampling strategy that allows fields with hundreds of horizontal wells to be handled.

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Section: Well Tvd Uncertainty Modelmentioning

confidence: 99%

Simultaneous Prediction of Geological Surfaces and Well Paths

Dahle¹,

Almendral-Vasquez²,

Abrahamsen³

2015

Proceedings

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Consistent prediction of well paths and geological surfaces

Vázquez,

Dahle,

Abrahamsen

et al. 2024

Comput Geosci

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We propose a smooth stochastic process for modeling the vertical well path uncertainty. This process describes the accumulation of measurement errors along the well path. We combine the stochastic process with a stochastic model for surfaces into a consistent framework for simultaneous prediction of well paths and surfaces. We show properties of the proposed stochastic process and provide examples of interaction between wells and surfaces.

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