Exploring the Capability of Temperature-Only Analysis for Zonal Flow Quantification

Abstract

Abstract Temperature logs have been used to monitor producing wells since the early 1930s. Normally, analysis of the temperature log is viewed as secondary to that of the spinner flowmeter, which gives flow velocity directly, and temperature is conventionally used only as an indicator of fluid entry/exit with the production logging tool (PLT). The main disadvantage of the PLT is that if spinner flowmeter data are not good due to tool problems, then flow quantification is jeopardized. Additionally, in recent years, the cost of production logging has increased considerably because many wells are now drilled horizontally through the reservoir, and the PLTs must be conveyed on coiled tubing or well tractors, and, in some cases (subsea wells), even this may not be possible. Consequently, alternative technologies become viable if they can be used for flow quantification using just temperature data. This paper presents a new flow quantification model using temperature data acquired using production logging or a distributed temperature sensor (DTS) system. The model presented in this paper can handle multiple production zones with their zonal fluid properties as input to give corresponding zonal flow rates as output. The said model is applicable for single-phase oil and gas producer wells as well as water injection wells in both onshore and offshore environments. There are two modes of flow calculation for each answer product-steady state or transient. The model is integrated into easy-to-use software, and it has options for forward simulation as well as optimization. The forward simulation calculates temperature distribution along the wellbore for any given production profile, which is critical for model calibration for any reservoir. After the model has been validated for a reservoir, it can be used for zonal flow quantification using any temperature survey. The objective of the optimization option is to allow the user to fit the model output temperature curve to a selected temperature curve by means of a genetic fitting algorithm that will adjust one or two user-selected reservoir parameters, such as permeability, pressure, skin, gas-oil ratio (GOR), temperature, or water-cut, until a fit is achieved. The model has been extensively tested against synthetic, literature and field examples and good agreements have been obtained, confirming the robustness of this novel approach.