Real Time Reservoir Fluid Log from Advanced Mud Gas Data

Abstract

Abstract In a recent paper (Yang et al., 2019a), we published a machine learning method to quantitatively predict reservoir fluid properties from advanced mud gas (AMG) data. This approach has clear advantages due to early access, low cost, and a continuous reservoir fluid prediction for all reservoir zones. In this paper, we demonstrate how real time reservoir fluid logs are generated and compare the results with PVT samples or production data from the same well. We develop a workflow of generating reservoir fluid logs from AMG data and PVT database. The workflow consists of two main processes; first a quality assessment of AMG data and second the computation of reservoir fluid properties (in this paper we use gas oil ratio). The entire workflow is written in python and embedded into existing commercial petrophysics softwares. The final product of the workflow are three log tracks which we call the reservoir fluid logs and those are 1) the concentration readings of the AMG data, 2) the QC metric score, and 3) the predicted GOR log. These three logs are plotted together with other standard open hole logs such as gamma ray, neutron-density, sonic and resistivity log to get a more comprehensive formation evaluation. Reservoir fluid logs derived from AMG data has two main advantages. First, it is the only approach to acquire continuous reservoir fluid properties along the well path. The continuous fluid profile can be used to understand the variation of reservoir fluids in both vertical and lateral direction. The second advantage is that the reservoir fluid log is obtained while drilling and therefore the information can be used to optimize the drilling process or the downhole sampling program during wireline operation. In this paper, we demonstrate the application of the reservoir fluid logs in four conventional field cases. In the first study case we show the benefit of using the reservoir fluid logs in a horizontal well as a substitute for downhole fluid sampling. In the second case study, we demonstrate how the reservoir fluid log is utilized to optimize the downhole fluid sampling program which results in reducing the subsurface uncertainty. Next, we exhibit the use of the reservoir fluid logs to locate gas oil contact in a case where pressure data does not show clear distinction of gas and oil gradient in the reservoir. In the last example, we illustrate the use of reservoir fluid knowledge from AMG to characterizing the fluid variation across a field. The field applications demonstrate the success of the new method for conventional reservoirs, provided good-quality AMG data are available.