Analysis of Pressure Data From Heterogeneous Reservoirs: Wellbore Storage and Skin Effects

1994 ◽  
Author(s):  
Lifu Chu ◽  
A.C. Reynolds
Keyword(s):  
Pressure Data ◽  
Heterogeneous Reservoirs ◽  
Wellbore Storage

scholarly journals Explicit Deconvolution of Well Test Data Dominated by Wellbore Storage

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
K. Razminia ◽  
A. Hashemi ◽  
A. Razminia ◽  
D. Baleanu
Keyword(s):  
Test Data ◽  
Material Balance ◽  
Early Time ◽  
Time Behavior ◽  
Well Test ◽  
Deconvolution Method ◽  
Pressure Data ◽  
Practical Applications ◽  
Wellbore Storage ◽  
Storage Effects

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.

Effect of Wellbore Storage on the Analysis of Multiphase Flow Pressure Data

1994 ◽  
Vol 9 (03) ◽  
pp. 219-227
Author(s):  
D.G. Hatzignatiou ◽  
A.M.M. Peres ◽  
A.C. Reynolds
Keyword(s):  
Multiphase Flow ◽  
Pressure Data ◽  
Wellbore Storage ◽  
Flow Pressure

Conditional Statistical Moment Equations for Dynamic Data Integration in Heterogeneous Reservoirs

2006 ◽  
Vol 9 (03) ◽  
pp. 280-288 ◽  
Author(s):  
Liyong Li ◽  
Hamdi A. Tchelepi
Keyword(s):  
Statistical Moment ◽  
Limited Data ◽  
Pressure Data ◽  
Moment Equations ◽  
Reservoir Properties ◽  
Incomplete Knowledge ◽  
Heterogeneous Reservoirs ◽  
Knowledge Uncertainty ◽  
Available Information ◽  
Permeability Field

Summary An inversion method for the integration of dynamic (pressure) data directly into statistical moment equations (SMEs) is presented. The method is demonstrated for incompressible flow in heterogeneous reservoirs. In addition to information about the mean, variance, and correlation structure of the permeability, few permeability measurements are assumed available. Moreover, few measurements of the dependent variable are available. The first two statistical moments of the dependent variable (pressure) are conditioned on all available information directly. An iterative inversion scheme is used to integrate the pressure data into the conditional statistical moment equations (CSMEs). That is, the available information is used to condition, or improve the estimates of, the first two moments of permeability, pressure, and velocity directly. This is different from Monte Carlo (MC) -based geostatistical inversion techniques, where conditioning on dynamic data is performed for one realization of the permeability field at a time. In the MC approach, estimates of the prediction uncertainty are obtained from statistical post-processing of a large number of inversions, one per realization. Several examples of flow in heterogeneous domains in a quarter-five-spot setting are used to demonstrate the CSME-based method. We found that as the number of pressure measurements increases, the conditional mean pressure becomes more spatially variable, while the conditional pressure variance gets smaller. Iteration of the CSME inversion loop is necessary only when the number of pressure measurements is large. Use of the CSME simulator to assess the value of information in terms of its impact on prediction uncertainty is also presented. Introduction The properties of natural geologic formations (e.g., permeability) rarely display uniformity or smoothness. Instead, they usually show significant variability and complex patterns of correlation. The detailed spatial distributions of reservoir properties, such as permeability, are needed to make performance predictions using numerical reservoir simulation. Unfortunately, only limited data are available for the construction of these detailed reservoir-description models. Consequently, our incomplete knowledge (uncertainty) about the property distributions in these highly complex natural geologic systems means that significant uncertainty accompanies predictions of reservoir flow performance. To deal with the problem of characterizing reservoir properties that exhibit such variability and complexity of spatial correlation patterns when only limited data are available, a probabilistic framework is commonly used. In this framework, the reservoir properties (e.g., permeability) are assumed to be a random space function. As a result, flow-related properties such as pressure, velocity, and saturations are random functions. We assume that the available information about the permeability field includes a few measurements in addition to the spatial correlation structure, which we take here as the two-point covariance. This incomplete knowledge (uncertainty) about the detailed spatial distribution of permeability is the only source of uncertainty in our problem. Uncertainty about the detailed distribution of the permeability field in the reservoir leads to uncertainty in the computed predictions of the flow field (e.g., pressure).

scholarly journals Pressure Data Analysis and Multilayer Modeling of a Gas-Condansate Reservoir

2015 ◽  
Vol 2 (1) ◽  
pp. 7-16
Author(s):  
Fatema Akter Happy ◽  
Mohammad Shahedul Hossain ◽  
Arifur Rahman
Keyword(s):  
Cross Flow ◽  
Storage Effect ◽  
Pressure Data ◽  
Reservoir Properties ◽  
Gas Field ◽  
Wet Gas ◽  
Wellbore Storage ◽  
Response Pressure ◽  
Gas Fields ◽  
Skin Storage

Kailastila gas field located at Golapgonj, Sylhet is one of the largest gas fields in Bangladesh. It produces a high amount of condensate along with natural gas. For the high values of GOR, it may be treated as a wet gas at reservoir condition. Three main sand reservoirs are confirmed in this field (upper, middle & lower).In this study, it has been shown that reservoir parameters of this gas field can be obtained for multilayered rectangular reservoir with formation cross-flow using pressure and their semi log derivative on a set of dimensionless type curve.The effects of the reservoir parameters such as permeability, skin, storage coefficient, and others such as reservoir areal extent and layering on the wellbore response, pressure are investigated.Shut in pressures are used in calculating permeability, skin factor, average reservoir pressure, wellbore storage effect and other reservoir properties. The direction of the formation cross flow is determined, first by the layer permeability and later by the skin factor.Finally, it is recommended to perform diagnostic analysis along with multilayer modeling to extract better results.Reservoir can also be considered as a multilayer cylindrical and can also use these models for other fields.

Analysis of Short-Time Pressure Data Dominated by Wellbore Storage and Skin

1979 ◽  
Vol 31 (05) ◽  
pp. 623-631 ◽  
Author(s):  
J. Garcia-Rivera ◽  
Rajagopal Raghavan
Keyword(s):  
Time Pressure ◽  
Pressure Data ◽  
Wellbore Storage ◽  
Short Time

Fast-Fourier-Transform-Based Deconvolution for Interpretation of Pressure Transient Test Data Dominated by Wellbore Storage

2005 ◽  
Vol 8 (03) ◽  
pp. 224-239 ◽  
Author(s):  
Yueming Cheng ◽  
W. John Lee ◽  
Duane A. McVay
Keyword(s):  
Laplace Transform ◽  
Early Time ◽  
Pressure Response ◽  
Pressure Data ◽  
Reservoir System ◽  
Wellbore Storage ◽  
Constant Rate ◽  
Storage Effects ◽  
Pressure Transient Test ◽  
Deconvolution Techniques

Summary We present a deconvolution technique based on a fast-Fourier-transform (FFT)algorithm. With the new technique, we can deconvolve "noisy" pressure and rate data from drawdown and buildup tests dominated by wellbore storage. The wellbore-storage coefficient can be variable in the general case. In cases with no rate measurements, we use a "blind" deconvolution method to restore the pressure response free of wellbore-storage effects. Our technique detects the afterflow/unloading rate function with Fourier analysis of the observed pressure data. The technique can unveil the early-time behavior of a reservoir system masked by wellbore-storage effects, and it thus provides a powerful tool to improve pressure-transient-test interpretation. It has the advantages of suppressing the noise in the measured data, handling the problem of variable wellbore storage, and deconvolving the pressure data without rate measurement. We demonstrate the applicability of the method with a variety of synthetic and actual field cases for both oil and gas wells. Some of the actual cases include measured sandface rates (which we use only for reference purposes), and others do not. Although this paper is focused on deconvolution of pressure-transient-test data during a specific drawdown/buildup period corresponding to an abrupt change of surface flow rate, the deconvolution method itself is very general and can be extended readily to interpret multirate test data. Introduction In conventional well-test analysis, the pressure response to constant-rate production is essential information that presents the distinct characteristics for a specific type of reservoir system. However, in many cases, it is difficult to acquire sufficient constant-rate pressure-response data. The recorded early-time pressure data are often hidden by wellbore storage(variable sandface rates). In some cases, outer-boundary effects may appear before wellbore-storage effects disappear. Therefore, it is often imperative to restore the early-time pressure response in the absence of wellbore-storage effects to provide a confident well-test interpretation. Deconvolution is a technique used to convert measured pressure and sandface rate data into the constant-rate pressure response of the reservoir. In other words, deconvolution provides the pressure response of a well/reservoir system free of wellbore-storage effects, as if the well were producing at a constant rate. Once the deconvolved pressure is obtained, conventional interpretation methods can be used for reservoir system identification and parameter estimation. However, mathematically, deconvolution is a highly unstable inverse problem because small errors in the data can result in large uncertainties in the deconvolution solution. In the past 40 years, a variety of deconvolution techniques have been proposed in petroleum engineering, such as direct algorithms, constrained deconvolution techniques, and Laplace-transform-based methods, but their application was limited largely because of instability problems. Direct deconvolution is known as a highly unstable procedure. To reduce solution oscillation, various forms of smoothness constraints have been imposed on the solution. Coats et al. presented a linear programming method with sign constraints on the pressure response and its derivatives. Kuchuk et al. used similar constraints and developed a constrained linear least-squares method. Baygun et al. proposed different smoothness constraints to combine with least-squares estimation. The constraints were an autocorrelation constraint on the logarithmic derivative of pressure solution and an energy constraint on the change of logarithmic derivative. Efforts also were made to perform deconvolution in the Laplace domain. Kuchuk and Ayestaran developed a Laplace-transform-based method using exponential and polynomial approximations to measured sandface rate and pressure data, respectively. Methods presented by Roumboutsos and Stewart and Fair and Simmons used piecewise linear approximations to rate and pressure data. All the Laplace-transform-based methods used the Stehfest algorithm to invert the results in the Laplace domain back to the time domain. Although the above methods may give a reasonable pressure solution at a low level of measurement noise, the deconvolution results can become unstable and uninterpretable when the level of noise increases. Furthermore, existing deconvolution techniques require simultaneous measurement of both wellbore pressure and sandface rate. However, it is not always possible to measure rate in actual well testing. Existing techniques are, in general, not suitable for applications without sandface rate measurement.

scholarly journals Revisão e Implementação de Soluções Analíticas para a Determinação da Pressão em Poços de Petróleo

2020 ◽  
Vol 5 ◽  
pp. 17
Author(s):  
Igor Caetano Cariello ◽  
Paulo de Tarço Honório Junior ◽  
Grazione De Souza ◽  
Helio Pedro Amaral Souto
Keyword(s):  
Laplace Transform ◽  
Analytical Solutions ◽  
Reservoir Engineering ◽  
Well Testing ◽  
Pressure Data ◽  
Test Analysis ◽  
Wellbore Storage ◽  
Reservoir Systems ◽  
The Laplace Transform ◽  
The Time Domain

<p>A Análise de Testes de Poços é um ramo da Engenharia de Reservatórios no qual<br />empregamos dados de pressão de poço a partir de testes de produção/injeção de fluido em conjunto com modelos físico-matemáticos para caracterizar o sistema poço-reservatório, usando problemas inversos. Nessas situações, aplicamos amplamente soluções analíticas e semianalíticas do modelo físico-matemático que descreve o fluxo. Nesse contexto, o objetivo do presente estudo é 1) realizar uma revisão bibliográfica sobre algumas das soluções analíticas clássicas para determinação da pressão no poço produtor e 2) implementar os códigos numéricos para a criação de uma biblioteca computacional, proporcionando as soluções analíticas voltadas para a determinação da pressão em poços produtores de petróleo. Os sistemas poço-reservatório estudados possuem um poço vertical e levam em consideração os efeitos de condições de contorno, a estocagem na coluna de produção do poço, dano à formação, períodos de fluxo e estática, bem como a presença de fraturas naturais. Obtivemos as soluções analíticas usando a transformada de Laplace e uma inversão numérica, utilizando o algoritmo Stehfest, para calcular a variação de pressão ao longo do tempo.</p><p><br /><strong>Palavras-chave</strong>: Soluções Analíticas, Transformada de Laplace Inversa, Tranformada de Laplace, Algoritmo de Stehfest, Análise de Teste de Poço.</p><p>===================================================================</p><p>Well Testing Analysis is a branch of Reservoir Engineering, in which we<br />employ well pressure data from production tests/fluid injection in conjunction with physical-mathematical models to characterize the well-reservoir system, using inverse problems. In these situations, we widely used analytical and semi-analytical solutions of the physical-mathematical model that describes the flow. In this context, the objective of this work is to 1) carry out a bibliographic review on some of the classic analytical solutions for determining the pressure in the producing well and 2) implement the numerical codes for the creation of a computational library, providing the analytical solutions aimed at determining pressure in oil-producing wells. The well-reservoir systems with a vertical well take into account the boundary effects, wellbore storage, formation damage, drawdown and buildup test analysis, and the presence of natural fractures. We obtain the analytical solutions using the Laplace transform and a numerical inversion, using the Stehfest algorithm, to calculate the pressure variation in the time domain.</p><p><br /><strong>Key words</strong>: Analytical Solutions, Inverve Laplace Transform, Laplace Transform, Stehfest Algorithm, Well Testing Analysis.</p>

Delivering Sustainable Water Systems Using Kalman Filter and Statistical Approach

2017 ◽  
Vol 4 (1) ◽  
pp. 41-52
Author(s):  
Dedy Loebis
Keyword(s):  
Kalman Filtering ◽  
Statistical Approach ◽  
Water Systems ◽  
Data Sets ◽  
Pressure Data ◽  
Burst Event ◽  
Sample Data ◽  
Other Information ◽  
Supply Systems ◽  
Frequency Features

This paper presents the results of work undertaken to develop and test contrasting data analysis approaches for the detection of bursts/leaks and other anomalies within wate r supply systems at district meter area (DMA)level. This was conducted for Yorkshire Water (YW) sample data sets from the Harrogate and Dales (H&D), Yorkshire, United Kingdom water supply network as part of Project NEPTUNE EP/E003192/1 ). A data analysissystem based on Kalman filtering and statistical approach has been developed. The system has been applied to the analysis of flow and pressure data. The system was proved for one dataset case and have shown the ability to detect anomalies in flow and pres sure patterns, by correlating with other information. It will be shown that the Kalman/statistical approach is a promising approach at detecting subtle changes and higher frequency features, it has the potential to identify precursor features and smaller l eaks and hence could be useful for monitoring the development of leaks, prior to a large volume burst event.

A Theoretical Model for Dynamic Performance Prediction of Air-Foam Flooding in Heterogeneous Reservoirs

2016 ◽  
Author(s):  
Jun Yang ◽  
Xiangzeng Wang ◽  
Shubao Wang ◽  
Ruimin Gao ◽  
Yizhong Zhang ◽  
...  
Keyword(s):  
Theoretical Model ◽  
Performance Prediction ◽  
Dynamic Performance ◽  
Heterogeneous Reservoirs ◽  
Foam Flooding
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