Experimental and Numerical Assessment of the Influence of Bottomhole Pressure Drawdown on Terrigenous Reservoir Permeability and Well Productivity

During oil and gas fields development,a decrease in reservoir and bottomhole pressure has often a detrimental effect on reservoir properties,especially permeability.This study presents the results of laboratory tests conducted to determine the response of terrigenous reservoir core-sample permeability to changes in the effective stresses and a decrease in the reservoir pressure.The considered samples were exposed for a long time to a constant high effective stress for a more reliable assessment of the viscoplastic deformations.According to these experiments,the decrease of the core samples permeability may reach 21%with a decrease in pressure by 9.5 MPa from the initial reservoir conditions.Numerical simulations have been also conducted.These have been based on the finite element modeling of the near-wellbore zone of the terrigenous reservoir using poroelasticity relations.The simulation results show a limited decrease in reservoir permeability in the near-wellbore zone(by 17%,which can lead to a decrease in the well productivity by 13%).

Prediction of Flowing Bottomhole Pressures for Two-Phase Coalbed Methane Wells

A method is proposed to predict the flowing bottomhole pressures （FBHPs） for two-phase coalbed methane （CBM） wells. The mathematical models for both gas column pressure and two-phase fluid column pressure were developed based on the well liquid flow equation. FBHPs during the production were predicted by considering the effect of entrained liquid on gravitational gradients. Comparison of calculated BHPs by Cullender-Smith and proposed method was also studied. The results show that the proposed algorithm gives the desired accuracy of calculating BHPs in the low- productivity and low-pressure CBM wells. FBHP is resulted from the combined action of wellhead pressure, gas column pressure and fluid column pressure. Variation of kinetic energy term, compressibility and friction factors with depth increments and liquid holdup with velocity should be considered to simulate the real BHPs adequately. BHP is a function of depth of each column segment. The small errors of less than 1.5% between the calculated and measured values are obtained with each segment within 25 m. Adjusting BHPs can effectively increase production pressure drop, which is beneficial to CBM desorption and enhances reservoir productivity. The increment of pressure drop from 5.37 MPa2 to 8.66 MPa2 leads to an increase of CBM production from 3270 m3/d to 6700 m3/d and is attributed to a decrease in BHP from 2.25 MPa to 1.33 MPa.

Predicting the effects of selected reservoir petrophysical properties on bottomhole pressure via three computational intelligence techniques

This study investigates the effects of selected petrophysical properties on predicting flowing well bottomhole pressure.To efficiently situate the essence of this investigation,genetic,imperialist competitive and whale optimization algorithms were used in predicting the bottomhole pressure of a reservoir using production data and some selected petrophysical properties as independent input variables.A total of 15,633 data sets were collected from Volvo field in Norway,and after screening the data,a total of 9161 data sets were used to develop apt computational intelligence models.The data were randomly divided into three different groups:training,validation,and testing data.Two case scenarios were considered in this study.The first scenario involved the prediction of flowing bottomhole pressure using only eleven independent variables,while the second scenario bothered on the prediction of the same flowing bottomhole pressure using the same independent variables and two selected petrophysical properties(porosity and permeability).Each of the two scenarios involved as implied in the first scenario,the use of three(3)heuristic search optimizers to determine optimal model architectures.The optimizers were allowed to choose the optimal number of layers(between 1 and 10),the optimal number of nodal points(between 10 and 100)for each layer and the optimal learning rate required per task/operation.the results,showed that the models were able to learn the problems well with the learning rate fixed from 0.001 to 0.0001,although this became successively slower as the leaning rate decreased.With the chosen model configuration,the results suggest that a moderate learning rate of 0.0001 results in good model performance on the trained and tested data sets.Comparing the three heuristic search optimizers based on minimum MSE,RMSE,MAE and highest coefficient of determination(R^(2))for the actual and predicted values,shows that the imperialist competitive algorithm optimizer predicted the flowing bottomhole pressure most accurately relative to the genetic and whale optimization algorithm optimizers.