The objective of this paper is to estimate the Bottom hole Pressure (BHP) from water level and other available parameters in CBM wells by (a) calibrating the dynamic water level (b) surveying the available literature (c) testing and comparing different models for wells in CBM Sohagpur field which have bottom hole gauges (d) selecting an appropriate model and finalizing the algorithm to estimate the BHP with reasonable accuracy and (e) automating the process to calculate the estimated BHP.
We have proposed a method for calibrating the water level taken by echo meter by calculating the acoustic velocity for actual conditions prevalent in a CBM well. We have then estimated the BHP for 3 wells of CBM Sohagpur field, operated by Reliance Industries limited in India, with the help of calibrated water level and other parameters using 4 different models (Podio, Godbey & Dimon, Gilbert models and Rashid Hasan, C. Shah Kabir and Rehana Rahman model) and compared the results of those models with the actual BHP provided by the bottom hole gauges
The algorithm when used with Rashid Hasan, C. Shah Kabir and Rehana Rahman model gave more accurate results irrespective of the production parameter in CBM wells. A maximum error of 3% and minimum error of 0.5% was observed in Rashid Hasan, C. Shah Kabir and Rehana Rahman model while the other models gave up to 25% error. Thus, we have finalized the algorithm to be used for estimation of BHP with minimal error in our case and we have automated the process which has helped the operator in calculating the estimated BHP from other parameters very easily.
We have proposed a method for calibration of water level for actual conditions, which is necessary to arrive at correct BHP, tested different models available for calculation of BHP for CBM wells and compared the results. We have proposed an algorithm for calculation of BHP from water level and other parameters in CBM wells and automated the process which has helped the operator immensely in optimizing the dewatering operation and improving the production forecasts.