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SPE-192002-MS
The purpose of this paper is to propose a representation and optimisation framework in CBM wells to recommend an optimal completion design. This method is novel and has the following advantages. Surrogate well model representation from time series production data. The representation is stationary and reflects key features of a coal seam
gas well such as tank volume, decline and gas liquid ratio. Use of a Gaussian Regression Optimisation framework that provides both a runlife estimate and an uncertainty estimate with a low number of observations. Utilise the exploration and exploitation of the regression function to design new completion types that have not been tried before and also recommend completion designs that offer the most runlife with the lowest uncertainty.
PCP Pump FailuresThere are many causes of PCP failure and table is comprehensive list is managed by C-FER which is a joint industry project created to enhance the performance of PCP pumps.
Table 1Downloaded from http://onepetro.org/SPEAPOG/proceedings-pdf/18APOG/2-18APOG/D021S016R002/1220497/spe-192002-ms.pdf/1 by Vedanta Limited - Cairn Oil & Gas user on 28 June 2023
SPE-192002-MS
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The focus of this modeling approach is isolated to the PCP design given the reservoir fluids and completion type. This is a practical consideration given that the operator is only able to choose the best pump design once the well is drilled and completed.
A central premise of this modeling approach is that pumps operating in the same environment will have similar runlives. However, the operating environment introduces
variability in runlife of PCPs, such as the differential pressures across the pump,
intake pressure, flowrates,
gas liquid ratios, gas
volume fractions,
solids volume fractions, pump off control, and water chemistry.
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