Waterflood Surveillance by Calibrating Streamline-Based Simulation with Crosswell Electromagnetic Data

Mishra, Sandipan; Shrivastava, Chandramani; Ojha, Aditya; Miotti, Fabio

doi:10.2523/19286-ms

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…As an important means of geophysical survey, the cross-well electromagnetic method can be used to detect the distribution of remaining oil and find oil and gas enrichment zones, to improve the success rate of drilling and enhance recovery efficiency. [1][2][3][4] In the past 20 years, cross-well detection technology has gradually developed into two kinds of detection methods with different physical mechanisms: cross-well seismic and cross-well electromagnetic detection. [5][6][7][8][9] The results show that, compared with the cross-well seismic method, the cross-well electromagnetic detection method is more sensitive to the changes in cross-well formation characteristics and fluid properties, and can directly provide the resistivity distribution information describe the reservoir cross-well fluid spatial distribution.…”

Section: Introductionmentioning

confidence: 99%

Research on cross‐well pseudorandom electromagnetic detection method and extraction of response characteristics

Song

Momayez

Wang

et al. 2020

Energy Science & Engineering

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Section: Introductionmentioning

confidence: 99%

Research on cross‐well pseudorandom electromagnetic detection method and extraction of response characteristics

Song

Momayez

Wang

et al. 2020

Energy Science & Engineering

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“…Такая схема измерений позволяет получить детальную картину электрических свойств межскважинного пространства в плоскости, проходящей через обе скважины. Совместная интерпретация совокупности полученных таким образом разрезов позволяет получить трехмерный образ электрических свойств среды [Кузнецов, 2012;Mishra et al, 2019].…”

Section: Introductionunclassified

Machine learning interpretation of inter-well radiowave survey data

Aleshin¹,

Malygin²

2019

CRM

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Enhancing Reservoir Characterization by Calibrating 3D Reservoir Model with Inter-Well Data in 2D Space

Mishra

Shrivastava

Ojha

et al. 2019

Day 2 Wed, April 10, 2019

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Until recently, reservoir characterization methods in the industry were limited to use of seismic technologies in exploration of oil and gas and had a very constrained role in production and development. In the past, using characterization for development fields was considered a very perilous task. Technological advancements and the risk-averse mindset have significantly expanded the application of reservoir characterization. Today, reservoir characterization is the basis of any development plans made for a commercial field. Development of 3D reservoir modeling techniques to generate field development plans (FDPs) marked a step-change in reservoir characterization methods. Introduction of geostatistics and numerical simulation made it possible to build precise models to generate realistic field development scenarios. This is the state-of-the-art seismic-to-simulation method of reservoir characterization used in FDPs today. However, the struggle to estimate reservoir properties spatially away from the well continues. Surface seismic data provide excellent areal coverage but do not provide the vertical resolution required for a fine-scale reservoir model. Geostatistical methods reduce the uncertainty in spatial distribution of petrophysical properties from pseudo-point supports (wells) but are not calibrated spatially between the wells. Correspondingly, the fluid saturation distribution and the parameters used in dynamically calculating the same during numerical simulation are not calibrated in the interwell space. This paper details necessary data acquisitions and methods of calibration of 3D reservoir model to reduce uncertainty in the interwell space. The data acquisition methods have been available for some time, but have rarely been electronically incorporated in the 3D reservoir model and have been largely used to analytically guide the modeling and its inferences. A logical way of interpreting the results of acquisitions and calibrating the 3D reservoir model cell-by-cell is detailed in this paper.

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Waterflood Surveillance by Calibrating Streamline-Based Simulation with Crosswell Electromagnetic Data

Cited by 4 publications

References 17 publications

Research on cross‐well pseudorandom electromagnetic detection method and extraction of response characteristics

Research on cross‐well pseudorandom electromagnetic detection method and extraction of response characteristics

Machine learning interpretation of inter-well radiowave survey data

Enhancing Reservoir Characterization by Calibrating 3D Reservoir Model with Inter-Well Data in 2D Space

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