Analysis of Well Test Data From Permanent Downhole Gauges by Deconvolution

Schroeter, Thomas von; Hollaender, Florian; Gringarten, Alain C.

doi:10.2118/77688-ms

Cited by 45 publications

(31 citation statements)

References 5 publications

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“…The deconvolution method employed in this paper was developed by others (von Schroeter et al, 2001 and2002;Levitan, 2005 and2006). The benefits of pressure-rate deconvolution include providing an equivalent representation of variable-rate well test data in the form of a characteristic constant rate drawdown response.…”

Section: Reservoir Characterization Using Well Testingmentioning

confidence: 99%

Enhancement of Vorwata Field Reservoir Model by Integration of Pressure Transient Analysis with Real-Time Downhole Pressure Data

Setiawan¹,

Hird²,

Bennett³

2011

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Vorwata is a giant gas field located in Bintuni Bay, Papua Barat Province, Indonesia. The field was discovered in 1997 and currently produces approximately 1.3 bcf/d of dry gas from the Roabiba sandstone reservoir. Initial development consists of 14 wells drilled from two platforms. The wells produce through 7-inch tubing and have been tested up to 240 mmscf/d. A Permanent Downhole Pressure Sensor (PDPS) is installed in each well to continuously monitor downhole pressure and temperature.Vorwata field is a faulted, high sand quality reservoir. The impact of faults on reservoir performance was very uncertain prior to startup. There was great concern the field could be compartmentalized, or at least strongly influenced by flow baffles and barriers. One of the primary subsurface objectives during pre-startup testing was to assess lateral and vertical reservoir connectivity within the development area.Pressure build-up tests (PBU) were conducted in each well as part of the initial clean up and testing procedure. The initial tests were very helpful in characterizing offset faults. PBUs obtained once the field was put on production are influenced by interference from offset wells, masking any boundary effects. Interpretation of the PBUs is enhanced by applying deconvolution. A pre-startup interference test successfully identified lateral and vertical pressure communication within the development area. The well/fault geometries and reservoir connectivity characteristics obtained from the PBUs and interference test have been used to condition the static and dynamic models.This paper describes the well testing program that has been implemented to reduce reservoir uncertainty in the Vorwata reservoir during the early phase of field development. Well test and pressure interference results have been integrated into full-field models to better assess future field performance and development decisions.

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Section: Reservoir Characterization Using Well Testingmentioning

confidence: 99%

Enhancement of Vorwata Field Reservoir Model by Integration of Pressure Transient Analysis with Real-Time Downhole Pressure Data

Setiawan¹,

Hird²,

Bennett³

2011

All Days

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“…In recent years, deconvolution algorithm has attracted the most of attention in the research of transient pressure analysis (Roumboutsos and Stewart, 1988;Hollaender et al, 2001;Schoreter et al, 2002;Levitan et al, 2004Levitan et al, , 2006Zheng and Wang, 2009). Its initial application is for the analysis of pressure build-ups due to multiple rate flowing history.…”

Section: Analysis Approaches For Non-linear Transient Pressure (Synthmentioning

confidence: 99%

Diagnosis and Analysis of Nonlinear Transient Pressure From Permanent Down hole Gauges (PDG)

Zheng¹

2010

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Long term transient pressure from a permanent down-hole gauge (PDG) is a combined response of the reservoir and many other sources. These include interference from other wells nearby, multi-phase flow and skin changing along with the production process, which generated a non-linear pressure signal, making well test analysis a challenging task for engineers.If the transient pressure is non-linear, the traditional well testing principles, based on linear reservoir system, such as superposition and deconvolution are no longer valid for the analysis. In this case, engineer has three options in solving such well test problem:(1) Linearising the nonlinear problem, for example, using so called pseudo-pressure approach (2) Choosing a specially designed approach for this problem such as numerical well testing (3) Identifying or diagnosis for the cause given the nonlinearity of the reservoir response, then decompose the response to separate the linear and nonlinear components. Thereafter each of these can be analysed using the corresponding methods respectively. This paper presents primary study results on the last option (3) mentioned above. Two cases causing the non-linear reservoir response were investigated. One is the changing skin problem. Another is the PDG pressure influenced by interference.The study has demonstrated that before proceeding for any analysis, the diagnosis of the nonlinearity is essential to ensure the correct selection of the analysis method, particularly for handling long term transient data from a PDG. Examples include the changing skin case, if the resulting data are analysed using deconvolution algorithm with predefined constraints; the nonlinear response of the reservoir system can be smoothed out completely, leading to the wrong interpretation. While transient pressure combined with interference, deconvolution algorithm can't be applied at all.This nonlinearity diagnostic key step should be included in well testing best practice of the industry.

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“…The main difficulty is that the deconvolution algorithm is very sensitive to the data error (from the measured wellbore pressure and production rate), which shows an inherent property of instability Çınar et al, 2006). As far as we know, only three representative deconvolution algorithms with good stability are developed by von Schroeter et al (2002Schroeter et al ( , 2004, Levitan (2005); Levitan et al (2006) and , , respectively. They offer necessary stability to make the deconvolution as a viable tool for well-test analysis Çınar et al, 2006); wherein just the two representative deconvolution algorithms by von Schroeter et al (2002Schroeter et al ( , 2004 and Levitan (2005); Levitan et al (2006) have been implemented into Saphir as the pressure transient analysis module of KAPPA software due to their well performance.…”

Section: Introductionmentioning

confidence: 99%

“…As far as we know, only three representative deconvolution algorithms with good stability are developed by von Schroeter et al (2002Schroeter et al ( , 2004, Levitan (2005); Levitan et al (2006) and , , respectively. They offer necessary stability to make the deconvolution as a viable tool for well-test analysis Çınar et al, 2006); wherein just the two representative deconvolution algorithms by von Schroeter et al (2002Schroeter et al ( , 2004 and Levitan (2005); Levitan et al (2006) have been implemented into Saphir as the pressure transient analysis module of KAPPA software due to their well performance. Von Schroeter et al's deconvolution algorithm and Levitan et al's deconvolution algorithm are both based on the same concept of minimizing a nonlinear weighted least-square objective function, involving the sum of three mismatch terms of pressure, rate and curvature, for reconstructing the deconvolved pressure drop and its logarithmic derivative; their difference mainly lies in the aspects of model assumption and specific definition of objective functions.…”

Section: Introductionmentioning

confidence: 99%

“…Onur et al (2006) have presented an investigation of these three deconvolution algorithms, and discussed the specific features associated with the use of each algorithm. For the two deconvolution algorithms by von Schroeter et al (2002Schroeter et al ( , 2004 and Levitan (2005); Levitan et al (2006), it has to be assumed that the variable production rate function should be given in a stepwise manner. In contrast, Ilk et al's deconvolution algorithm allows for a general production rate function but with the restriction that the function must be transformed into Laplace space as an analytical formula.…”

Section: Introductionmentioning

confidence: 99%

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An improved deconvolution algorithm using B-splines for well-test data analysis in petroleum engineering

Liu

Han

et al. 2017

Journal of Petroleum Science and Engineering

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A B S T R A C TIlk et al.'s deconvolution algorithm using B-splines involves the Laplace transformation of the convolution equation with respect to production rate and wellbore pressure based on Duhamel principle. However, for common cases, the production rate function has "discontinuity" with respect to production time; it does not satisfy the precondition that the function to be transformed by Laplace transformation should be continuous. This inherent defect may directly cause enormous amount of computational time or even the failure of the numerical Laplace inversion in the deconvolution process. Based on these concerns, a fundamentally improved deconvolution algorithm using B-splines is presented here. In the convolution equation, the wellbore pressure derivative corresponding to constant unit production rate as the target of deconvolution is still represented by weighted summation of second-order B-splines; however, the computation process of the deconvolution is kept in the level of integral in the real time space instead of the Laplace space, for the reason that there will be no continuity requirement for the production rate function in the application of Duhamel principle for the deconvolution computation problem. According to the real production rate history, a technique of piecewise analytical integration is adopted for obtaining the elements of sensitivity matrix of a linear system with respect to weight coefficients; the linear system is generated by substituting the measured wellbore pressure data and corresponding variable production rate data into the convolution equation containing B-splines. The proposed direct analytical solution method of the integration for calculating the elements of the sensitivity matrix can not only guarantee the success of the deconvolution computation, but also can largely enhance the deconvolution computation speed. Moreover, in order to further improve the computation speed, a binary search method is also applied to find which production segments (with constant production rate) the measured wellbore pressure data points locate at in the deconvolution computation process. Another linear system with respect to weight coefficients for the regularization from Ilk et al.'s deconvolution algorithm is appended in order to overcome the effect of data errors. The two linear systems are combined together as an over-determined linear system, which can be solved by the least square method. Eventually, the reconstructed wellbore pressure and its derivative by B-splines corresponding to the constant unit production rate can be obtained.Numerical experimental tests demonstrate that the improved deconvolution algorithm exhibits good accuracy, computation speed and stability of data error tolerance. And the statement on how to perform the regularization when data error exists is also made in order to deconvolve the correct wellbore pressure derivative. The improved deconvolution algorithm is also applied into an actual field example. It is found that the deconvolution results by the i...

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Analysis of Well Test Data From Permanent Downhole Gauges by Deconvolution

Cited by 45 publications

References 5 publications

Enhancement of Vorwata Field Reservoir Model by Integration of Pressure Transient Analysis with Real-Time Downhole Pressure Data

Enhancement of Vorwata Field Reservoir Model by Integration of Pressure Transient Analysis with Real-Time Downhole Pressure Data

Diagnosis and Analysis of Nonlinear Transient Pressure From Permanent Down hole Gauges (PDG)

An improved deconvolution algorithm using B-splines for well-test data analysis in petroleum engineering

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