Estimating reservoir properties using downhole temperature and pressure data

Abstract

Predicting the behavior of an underground reservoir requires precise reservoir properties determination. One of the most effective tools for this purpose is well test analysis. Conventional well test analysis employed bottomhole pressure data to understand the reservoir performance better. As the heterogeneity of the system increases, the complexity of well test analysis increases and nonunique solutions can be obtained. Using another source of data, like bottomhole temperature and wellbore temperature profile, can significantly minimize the uncertainty in reservoir model identification, particularly in more heterogeneous reservoirs like multi-layered systems.In the current study, the permeability of various reservoir models has been estimated via optimization approaches. For this purpose, uncertain reservoir parameters have been considered as optimization variables. The particle swarm optimization (PSO) and differential evolution (DE) algorithms have been utilized to find the optimal value of these parameters. The temperature and pressure data have been assumed as the input parameters and were simultaneously analyzed to determine the properties of reservoir layers more accurately. Some statistical parameters were employed to verify the accuracy of the developed approaches. Using simultaneous analysis of the bottomhole data and wellbore temperature profile, the average absolute percent relative error (AAPRE) values for the multi-layered heterogeneous reservoir model was 1.57%. This approach, compared to the bottomhole pressure analysis technique, in which AAPRE of 19.91% was obtained, led to significantly more reliable results.Furthermore, the best history-matched models created through various analysis approaches were selected to perform the prediction scenarios. Absolute relative error (ARE) values for the multi-layered heterogeneous reservoir model for bottomhole pressure analysis and simultaneous analysis were 12.00% and 0.26%, respectively. The results demonstrated that employing the simultaneous bottomhole temperature and pressure analysis approach has the lowest estimations error, reducing the uncertainties in the reservoir model predictions.