A New Automated Algorithm for Designing the Optimal Single-Well-Chemical-Tracer SWCT Tests At Various Reservoir Conditions

Abstract

Abstract Single-well-chemical-tracer (SWCT) is the most commonly used field method to determine oil or water saturation in one-spot pilot. The SWCT method overcomes the limitations of the well-to-well tracer method, being long measuring time and excessive tracer dispersion. However, other limitations are encountered such as the shorter investigation region and the effect of the product acid due to the hydrolysis reaction on the reliability of the saturation measurement. The latter one is still considered to be a controversial issue. Saturation measurements from large investigation regions are more reliable because of larger measured volume and longer distance away from the wellbore damage and capillary desaturated zone. However, some criteria prevent the test from reaching the desired investigation region and may thus cause deviations in the test results from the ideal test. These criteria are the higher fraction of hydrolyzed ester and the detection limit of ester, hydrolysis reaction during injection and production times, and fluid drift and tracer dispersion during shut-in time. The main objective of this research is to develop an automated algorithm to design the optimal SWCT test including the larger investigation region and more ideal tracer profile according to the reservoir conditions. The devised algorithm has been programmed based on the six different stages. In the first four stages, all test design parameters in different investigation regions and retardation factors are calculated. The test design parameters are sizing the test volume, test timing (i.e., injection, shut-in, and production), tracer concentration during the test, and the mean residence volume. In the fifth stage, all criteria are taken into consideration to find the optimal test design. After that, the achieved parameters are applied in the simulation stage (sixth stage) of the workflow. The objective of the final stage is the investigation of the effect of the following items on the tracer result in order to develop more ideal (i.e., Gaussian) tracer profiles: 1) ester bank and concentration, 2) ester properties, 3) shut-in time and adapting the conventional rule to the reservoir conditions. To evaluate the workflow methodology, two different field test cases with different reservoir conditions are employed in order to reflect the influence of different stages of the algorithm. After the numerical interpretation of these two tests, the algorithm is performed to design the optimal test with all criteria taken into account. The results show that the proficiency of the algorithm could be noticeable in the design of an optimal test with larger investigation region yielding more Gaussian-like profiles by the means of modifying the conventional rule of the shut-in time and selecting an optimal ester bank and concentration fitting to the reservoir conditions.