Drilling wells for oil/gas has been increasingly challenging with the companies moving towards difficult environments, such as in Tarim basin of China, some reservoirs buried so deeply (>7,000m) that we experience high temperature and pressure. The problems faced in these locations range from very narrow margin between pore (or collapse) and fracture pressure. The density of drilling fluid is often affected by HTHP, the careful research on the drilling fluids density at HTHP is very important for precisely predicting ESD as well as controlling the downhole pressure. A utility calculation model of drilling fluids density and ESD was proposed, which can predict the HTHP density and ESD.
Firstly, a new utility artificial neural network HTHP drilling fluid density prediction model was established based on the traditional BP neural network and PSO (Particle Swarm Optimization) optimization method. Then PSO-BP neural network HTHP drilling fluid density prediction model was proposed, in which the influence of drilling fluid component (oil phase, water phase volume fraction) was taken into account. Available experimental measurements of water-based and oil-based drilling fluids at pressure ranging from 0-96MPa and temperatures up to 183°C were used to develop and the PSO-BP network model and then the network weights, threshold parameters. Through this model the high-precision HTHP drilling fluids density can be obtained easily with the knowledge of the drilling fluids component data (oil phase and water phase volume fractions) and its density at standard conditions(0MPa, 20°C) based on the basic principle of PSO-BP network. Moreover, an new comprehensive ESD calculation model of HTHP well was established, which is applicable for all common type drilling fluids and through we can obtained the ESD profile of the well easily.
The prediction of this model has been compared with an extensive set of data from literature, the comparisons of different fluids density in HTHP show very good agreement, the prediction accuracy was improved, and in which the maximum average absolute error of predictions is less than 0.005sg. Finally, the proposed model has been applied for HTHP drilling fluids density and ESD prediction in several wells of the Tarim basin in China, the results show that the proposed model can exactly provide the HTHP drilling fluids density and ESD profile.
This study proposed an utility calculation of HTHP drilling fluids density and ESD profile while drilling, based on the new PSO-BP neural network, the optimal network weights, threshold parameters of was obtained, through which the high-precision drilling fluids density and ESD can be obtained easily. Moreover, the model has been verified and applied for field monitoring. Therefore, this model can be applied to provide more accurate predictions of HTHP drilling fluids density and ESD while drilling.