Deconvolution of Well Test Data in Lean and Rich Gas Condensate, and Volatile Oil Wells below Saturation Pressure

Hashemi

Abstract and Applied Analysis

et al. 2014

This paper addresses some methods for interpretation of oil and gas well test data distorted by wellbore storage effects. Using these techniques, we can deconvolve pressure and rate data from drawdown and buildup tests dominated by wellbore storage. Some of these methods have the advantage of deconvolving the pressure data without rate measurement. The two important methods that are applied in this study are an explicit deconvolution method and a modification of material balance deconvolution method. In cases with no rate measurements, we use a blind deconvolution method to restore the pressure response free of wellbore storage effects. Our techniques detect the afterflow/unloading rate function with explicit deconvolution of the observed pressure data. The presented techniques can unveil the early time behavior of a reservoir system masked by wellbore storage effects and thus provide powerful tools to improve pressure transient test interpretation. Each method has been validated using both synthetic data and field cases and each method should be considered valid for practical applications.

Section: Introductionmentioning

confidence: 99%

Explicit Deconvolution of Well Test Data Dominated by Wellbore Storage

Hashemi

Abstract and Applied Analysis

et al. 2014

SPE Eastern Regional Meeting

Production Analysis of a Shale Gas Reservoir Using Modified Deconvolution Method in the Presence of Sorption Phenomena

Kim

Jang

Ertekin

et al. 2015

When analyzing production data during reservoir characterization, pressure transient analysis requires a constant production rate during the entire production period. However, because of the extremely low permeability of shale formations, the flow regime changes over the long period of production. Therefore, it is rarely possible to acquire production data at a constant rate. In addition, as pressure declines with production in a shale gas reservoir, desorption of gas occurs, leading to nonlinearities due to large changes in gas compressibility, and it should be linearized for the use of deconvolution. In this study, we suggest sorption correction for pseudopressure using material balance equation to linearize the pseudopressure–rate relationship. By using this sorption correction term for pseudopressure in the deconvolution, we carried out pressure transient analysis with production data from a shale gas well to obtain the reservoir characteristics. The results show that, without sorption correction, the pseudopressure decline and its derivative varied depending on the magnitude of the desorption. However, with the correction applied, the curves were almost identical regardless of desorption. In the results of the deconvolution analysis with and without the sorption correction, pseudo-linear and pseudo-pseudosteady state flow periods appeared at different times, which explains the anomalous results in the pressure transient analysis. By implementing those results, we analyzed production data acquired from Fayetteville shale. The results show that the reservoir permeability, stimulated reservoir volume, and fracture half-length depended on the use of sorption correction for pseudopressure.

Field Applications of Constrained Multiwell Deconvolution

Jaffrezic

SPE Europec Featured at 81st EAGE Conference and Exhibition

Cumming

et al. 2019

Objectives/Scope This paper applies a new constrained multiwell deconvolution algorithm to two field cases: a gas reservoir with two producers, and an oil reservoir with three producers and one injector. Responses given by the constrained multiwell deconvolution are compared with simulations from history-matched reservoir models. Methods, Procedures, Process Permanent downhole pressure gauges are routinely installed in most new wells. The resulting large datasets are usually underexploited, however, because it is near impossible to extract information with conventional techniques in the case of well interferences. Multiwell deconvolution (Levitan, 2006; Cumming et al., 2013) solves this problem by providing constant-rate derivative responses of individual wells as if they were producing alone, and interference effects from any one well to any other. The deconvolved responses have the same durations as the corresponding pressure histories, and show features not visible in any of the test build up or drawdown periods. Results, Observations, Conclusions The published multiwell deconvolution algorithms are extensions of the single-well deconvolution algorithm from von Schroeter et al. (2001, 2004), which has become an essential component of the well test analysis toolbox. The Cumming et al. (2013) multiwell deconvolution algorithm has been successfully applied to a North Sea reservoir by Thornton et al. (2015). Multiwell deconvolution, however, is strongly overdetermined and the algorithm may not converge to the true solutions. It may also yield solutions that can reproduce pressure histories equally well while being non-physical. This is especially true when dealing with real field data that may be affected by noise on pressure and rate data. In order to reduce non-uniqueness and ensure physically feasible results, Cumming et al. (2019) incorporated additional constraints on the shapes of the deconvolved derivatives to discourage non-physical solutions. They also introduced additional knowledge on the reservoir, for instance, if it is a closed system, all deconvolved derivatives should tend towards a common unit slope at some future time. They illustrated the application of their enhanced algorithm with synthetic examples with known solutions involving up to nine wells. In this paper, the constrained multiwell algorithm is successfully applied to both a gas and an oil reservoir. Novel/Additive Information By extracting well and interwell reservoir signatures, multiwell deconvolution allow identification of compartmentalization or unanticipated heterogeneities very early in field life, making it possible to adjust the field development plan and the locations of future wells. In addition, it can accelerate the history-matching process by doing it on constant rate pressure responses rather than on complex production histories. An added advantage is that the comparison between the model derivatives and the actual deconvolved derivatives enables identification of mismatch causes.