Analysis Of Pressure Transient Data Research Articles

An automated data-driven pressure transient analysis of water-drive gas reservoir through the coupled machine learning and ensemble Kalman filter method

Natural gas plays a crucial role in sustaining the economic development. The production behavior of natural gas reservoir is significantly affected by the connecting aquifer. To investigate the aquifer and reservoir properties, the pressure buildup well test is conducted and the pressure transient data need to be analyzed. However, the pressure transient data analysis usually involves a procedure to build an analytical or numerical model to predict the pressure transient dynamics. With the existence of many historical records on the well test data and advancement of machine learning method, a data-driven method is proposed here to automate the well test data analysis of water-drive gas reservoir. The dataset generated from the previous well test records is used as the training set for the machine learning method. To identify the water invasion mode, the random forest classification method is introduced, and the discrete linear segment slopes are extracted from the pressure derivative curve as the feature. To predict the pressure transient dynamics, the random forest regression method is proposed to construct a projection between the aquifer/reservoir properties and the pressure transient curves. Once the water invasion mode is determined and the data-driven model to predict the pressure transient dynamic is constructed, the ensemble Kalman filter is used to estimate the aquifer/reservoir properties from the well testing data. The results show that the random forest classification method can accurately identify the water invasion mode and the random forest regression based ensemble Kalman filter method can estimate the aquifer/reservoir properties accurately with the reduced uncertainty.

A generalized approach for fast estimation of reservoir permeability distribution from analysis of pressure transient data

Estimation of permeability variation from analysis of pressure transient data is very challenging. Such problem may not have a unique solution and cannot be analyzed with classic well-test models. The available models for fast estimation of permeability map are restricted to several assumptions such as existence of pure radial or linear flow in an infinite medium. Such assumptions may be violated in real applications. To handle pressure transient analysis in heterogeneous reservoirs, more flexible models are needed to be applicable for majority of real well tests.In this work, a generalized approach is developed to estimate reservoir permeability variation from analysis of pressure transient data. For non-radial flow regimes, we show that the permeability rings should be defined non-circular. In flow geometries other than radial, we may not be able to use radius of investigation. Accordingly, we propose the use of pressure drop at the boundary of region of investigation instead. The average permeability in non-circular rings can produce satisfactory derivative predictions. Inspired by this observation, we developed an inverse procedure to estimate equivalent average permeability along with appropriate inner and outer ring boundaries. After estimation of equivalent permeabilities from the inverse procedure, the 2D permeability map is estimated through a minimization process. The defined objective function represents the difference of geometric average of realizations and equivalent average permeabilities from the inverse procedure. Using such form of definition, there is no need to perform any flow simulation during minimization process.We applied the procedure to back calculate the permeability variation around a horizontal well from analysis of its pressure derivative data. In addition, the uncertainty of the estimation and prediction was evaluated. The results reveal the strength of the proposed methodology for pressure transient analysis of heterogeneous reservoirs.

On the Analysis of Well Test Data Influenced by Capillary Pressure

The focus of this study is on the analysis of capillary pressure effects on well test data. Ignoring the capillary pressure is one of the assumptions that have been applied in all multiphase well test analysis approaches. In this work we investigate the influences of capillary pressure on the analysis of pressure transient data in a solution gas drive reservoir and in a reservoir containing oil and water. Reservoir simulations were performed to generate pressure and saturation data for each case and the pressure responses and the fluids saturation distribution for drawdown and build-up tests for two cases with and without capillary pressure are compared. According to the results of this study, it has been concluded that the influences of capillary pressure on fluids saturation behavior in build-up test due to importance of capillary force than viscose force in this period is more noticeable. Though the effects of capillary pressure lead to differences in the fluids saturation behavior but the authors have not observed dissimilarity in the pressure responses affected by capillary pressure for draw down and build-up test in the both reservoirs under study.

Explicit Deconvolution of Wellbore-Storage-Distorted Well-Test Data

This article, written by Technology Editor Dennis Denney, contains highlights of paper SPE 103216, "Explicit Deconvolution of Wellbore-Storage- Distorted Well-Test Data," by O. Bahabanian, SPE, D. Ilk, SPE, N. Hosseinpour-Zonoozi, SPE, and T.A. Blasingame, SPE, Texas A&M U., prepared for the 2006 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 24–27 September. The analysis and interpretation of wellbore-storage-distorted pressure-transient-test data remain significant challenges. Deconvolution (i.e., conversion of a variable-rate-distorted pressure profile into the pressure profile for an equivalent constant-rate-production sequence) has been in limited use as a conversion mechanism for the last 25 years. Unfortunately, standard deconvolution techniques require accurate measurements of flow rate and pressure at downhole (or sandface) conditions. While accurate pressure measurements are commonplace, the measurement of sandface flow rates is rare, essentially nonexistent in practice. An explicit (direct) deconvolution of wellbore-storage-distorted pressure-test data that uses only those pressure data was developed. Introduction To eliminate wellbore-storage effects in pressure-transient-test data, several methods have been proposed. An approximate direct method corrects the pressure-transient data distorted by wellbore storage into the equivalent-pressure function for the constant-rate case. Despite its simplicity, it has several shortcomings such as limited accuracy and erroneous skin-factor estimation. Rate-normalization techniques have been used to correct for wellbore-storage effects, and these methods were successful in some cases. The most appropriate application of rate normalization is its use for pressure-transient data influenced by continuously varying flow rates. Application of rate normalization requires sandface-flow-rate measurements and generally yields a shifted result trend that has the correct slope but incorrect intercept on a semilog graph (i.e., incorrect skin factor). Material-balance deconvolution has been shown to be a practical approach for the analysis of pressure-transient data distorted by wellbore-storage effects. In particular, this approach remedies the issue of a poor skin-factor estimate that typically is obtained with rate normalization. Material-balance deconvolution also is thought to require continuously varying sandface-flow-rate measurements. It is shown that sandface flow rates can be approximated from the observed pressure data. Methodology This work was put forth as an attempt to provide a set of simple, explicit deconvolution formulas that could be used on wellbore-storage-distorted pressure-transient-test data. The material-balance deconvolution was successful and should be considered sufficiently accurate for use as a practical tool for field applications. The other major method considered was the direct β-deconvolution algorithm modified to estimate the β-parameter from pressure rather than flow-rate data as originally proposed. The modification of the β-deconvolution algorithm, given only in terms of pressure variables, also was successful.

Analysis of Pressure-Transient Data From Underbalanced Perforating

Summary Determination of reservoir characteristics from transient pressures created by underbalanced perforating can enable an operator to decide whether a well is commercially productive before permanent completion. Several techniques exist to analyze such data. In this work, closed-chamber testing and log-log- and Cartesian-plotted short producing times are used to analyze data collected from a multiwell project in the Gulf of Mexico. Pressure data were obtained from underbalanced perforating and backsurge-washing treatments. Results of these analyses compared favorably with buildup tests performed after the wells were completed.