Interpretation of Cleanup Data in Gas-Well Testing From Derived Rates

Abstract

In drillstem testing (DST), analyses of transient data are generally restricted to the post-cleanup period because rates often go unmonitored at early times during the cleanup phase, unless aided by multiphase flow metering. The value of early-time production data monitoring may be overlooked for two reasons: first, transients may be difficult to interpret because of two-phase flow in the formation; second, two-phase flow metering becomes a requirement. The objective of this study is two-fold: to estimate rate from distributed-temperature sensor (DTS) data available for downhole telemetry with a rigorous transient analytical model, and to use the computed rates to perform transient analysis for both the pre- and post-cleanup periods. Comparing and contrasting permeability estimates from the two periods provides guidance on the suitability of this approach. To that end, this study compares and contrasts rate computation with analytical methods. The new methods entail transient-temperature modeling with and without superposition effects. Two field examples strengthened the new rate-computation methods introduced here, and simulated examples probed the possibility of extracting meaningful information from the cleanup data.

Combining Physics, Statistics, and Heuristics in the Decline-Curve Analysis of Large Data Sets In Unconventional Reservoirs

“…Because a large data set is being analyzed and the h 2 model is an infinite summation, computational time could be a concern. However, fast algorithms for the computation of the Jacobi h functions have been implemented in a number of high-level programming languages (Wolfram Research 1988;Igor 2007;Johansson et al 2013), and Mathematica was the one used in this work. As shown in Table 3, 992 wells can be successfully history matched in 109.3 seconds using an average desktop computer with eight cores computing in parallel.…”

“…The main concern in applying Eq. 7 in the decline-curve analysis of large data sets is the fact that it is an infinite summation, but there are computational routines available that are capable of computing it in a time-effective manner (Wolfram Research 1988;Igor 2007;Johansson et al 2013).…”