Pressure Transient Test Data Research Articles

Simultaneous inversion of permeability, skin and boundary from pressure transient test data in three-dimensional single well reservoir model

Simultaneous inversion of permeability, skin and boundary from pressure transient test data in three-dimensional single well reservoir model

Automatic reservoir model identification using syntactic pattern recognition in well test interpretation

Well test model identification is a challenging task due to the numerous types of well test interpretation models and the non-uniqueness of pressure responses generated by different reservoir models. An automated framework is crucial to aid in the identification of well test interpretation models. Since the identification of well test interpretation relies primarily on the various flow regimes appeared on different diagnostic plots. A novel approach is proposed for the well test model identification from the pressure transient test data using the syntactic pattern recognition in this study. In this study, the identification process of well test interpretation model is divided into six steps: preprocessing, feature primitive extraction, curve shape tracking, flow regime division, model preliminary inference, and model final validation incorporating TDS technology. The automatic identification framework developed with this method has been able to identify a variety of complex well test interpretation models correctly, and the non-uniqueness of model results can be well resolved by syntactic pattern recognition combined with TDS technology. In general, the findings of this study can help for better understanding of the process by which well test expert completes the task of model identification.

A robust deep structured prediction model for petroleum reservoir characterization using pressure transient test data

A robust deep learning model consisting of long short-term memory and fully connected neural networks has been proposed to automatically interpret homogeneous petroleum reservoirs having infinite, no flow, and constant pressure outer boundary conditions. The pressure change data recorded during the well test operation along with its derivative is input into the model to perform the classification for identifying the reservoir model and, further, regression to estimate output parameter. Gaussian noise was added to analytical models while generating the synthetic training data. The hyperparameters were regulated to perform model optimization, resulting in a batch size of 64, Adam optimization algorithm, learning rate of 0.01, and 80:10:10 data split ratio as the best choices of hyperparameters. The performance accuracy also increased with an increase in the number of samples during training. Suitable classification and regression metrics have been used to evaluate the performance of the models. The paper also demonstrates the prediction performance of the optimized model using simulated and actual oil well pressure drawdown test cases. The proposed model achieved minimum and maximum relative errors of 0.0019 and 0.0308, respectively, in estimating output for the simulated test cases and relative error of 0.0319 for the real test case.

Well-testing model identification using time-series shapelets

Abstract The well-testing model identification is a challenging task due to non-uniqueness of the pressure responses generated by different reservoir models. An automated framework is very useful to aid in diagnosing the well-testing interpretation models. Since the well-testing diagnostic plots are arranged nearly ordered in time, the well-testing model recognition could be considered as a time-series classification problem where different types of underlying reservoir models constitute different classes. A novel approach is proposed for the well-testing model identification from the pressure transient test data using the concept of shapelets in this article. Shapelets defined as time series subsequences which are in some sense maximally representatives of a class have been recently developed and used in time series classification and clustering problems. Five different well-testing models (or classes) are investigated in this article. Shapelets are initially extracted for each pair of classes using the Fast-Shapelet algorithm and the minimum distances from the time series objects to the extracted shapelets are calculated to form a new feature space. Classification is applied on the new feature space using four different binary classifiers including random forest (RF), support vector machine (SVM), logistic regression (LR) and probabilistic neural network (PNN). The results verify that the RF technique yields the highest F-score value in predicting the true class labels among all the binary classifiers. To further improve the classification performance, an ensemble learner is finally created using the individual classifiers. The proposed approach is validated by testing against several real field test data. The results indicate that the shapelet-based approach is promising for the well-testing diagnosis applications.

Spatial-Feature Identification and Parameter Estimation From Pressure-Transient Tests

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 142996, ’Grid-Based Inversion Methods for Spatial-Feature Identification and Parameter Estimation From Pressure-Transient Tests,’ by K.L. Morton, SPE, and R.J. Booth, Schlumberger; M. Onur, SPE, Istanbul Technical University; and F.J. Kuchuk, SPE, Schlumberger, prepared for the 2011 SPE Europec/EAGE Annual Conference and Exhibition, Vienna, Austria, 23-26 May. The paper has not been peer reviewed. Three grid-based inversion methods for estimating formation parameters and for spatial geological-feature identification based on pressure-transient-test (PTT) data from multiple-well locations were investigated. The first two methods use efficient adjoint schemes to determine the gradient of the objective functions. The third method uses ensemble Kalman filtering (EnKF) for data assimilation. With these methods, the existence and location of many subseismic features, such as strong spatial permeability variations, faults, fractures, and pinchouts, may be determined by use of exploration and production data. Introduction PTTs are used to determine the productivity of a well and the properties of the formation (reservoir) on the basis of downhole and/or surface pressure and flow-rate measurements. The main steps for interpretation are: Model identification—a possible set of reservoir models is found that may fit the data. Model-parameter estimation—model parameters are adjusted until the model behavior matches observed data. Model verification—consistency of the final model is verified by measuring the mismatch between the real system and the model or by comparing with other data. By use of conventional interpretation methods (i.e., semilog methods such as Horner or Miller-Dyes-Hutchinson, and/or type-curve matching of measured pressure and/or pressure derivative), data including reservoir pressure, an effective average permeability of the formation, skin factor, and wellbore storage can be estimated from the PTT data. In such interpretation, some sort of prior modeling is necessary to constrain the nonlinear parameter estimation because a model with many nonphysical reservoir parameters may match the observed PTT data. This prior knowledge may be available at small scale from logs and cores and at a larger scale from seismic surveys and outcrop analogies. Recently, nonlinear least-squares optimization has been applied to PTT data by use of numerical models with a similarly limited number of parameters—often models that are divided into a small number of regions, within which the reservoir parameters are assumed to be constant.

The Challenge of Model Identification in Well Test Interpretation—A Unique Build Up Analysis Case Study

Reservoir engineers are frequently faced with a very difficult question, thought to be a simple one, when attempting to analyze pressure transient test data. Correct model identification that can be used to analyze test data is ought to be a very simple exercise due to numerous analytical models, which are published in the literature. However, dealing with almost a black box situation “Underground Reservoir,” it is still difficult today to identify a reservoir model that represents a particular test. In fact, the pressure derivative technique in identifying flow regimes that pressure behavior undergoes during the well testing has introduced another dimension and is considered to be an important step in analyzing pressure transient tests. However, the non-uniqueness in model responses, which produces similar pressure derivatives, hinders the engineers, and most often causes in taking a wrong decision. This study looks into non-unique model response, introduces a simplistic method using numerical simulator and any reservoir information available in eliminating non-realistic solutions. Analysis of actual well test data, to obtain the very basic properties, and integrating any other available data, one can build different model realizations. An actual model was build by history matching the pressure derivative that helped reducing the problem of non-uniqueness response.

The interpretation of DST data for donghae-1 gas field, block VI-1, Korea

Donghae-1 gas field is located in Ulleung basin at offshore Ulsan, Korea, and its recoverable reserve is expected to be 170 to 200 BCF (Billion cubic feet). The field was confirmed to have potential gas and condensate reserves from an exploration well in 1998 and two appraisal wells in 1999. This field consists of five zones, with an average reservoir depth of about 7,000 to 8,000 ft. In this study, we have performed an analysis of Gorae V DST (Drillstem test) #2 for testing B4 zone which has the biggest reserves and Gorae V-1 DST #2 for testing B3 and B4 zones simultaneously among DST data achieved in a total of 11 zones at three wells. The pressure and flow rate recorded from two tested zones were used to obtain the reservoir characteristics and the well productivity. For pressure transient test data, we carried out the analysis of reservoir permeability, skin factor, wellbore storage effect and barrier effect by using the Homer plot and type curve matching methods. Also, with the deliverability test data, we estimated the absolute open flow which is the maximum flow rate of the gas well, and extracted the correlations representing production rate with reservoir pressure. According to the analysis, Gorae V DST #2 of B4 zone has a permeability and skin factor of 37 md (Millidarcy), 4.54, and Gorae V-1 DST #2 of B3 and B4 zones has 23 md and 21.0, respectively. It was also found that the wellbore storage effect was not significant for the two wells tested. From the deliverability test analysis, the AOF (Absolute open flow) of the Gorae V DST #2 is 152.8 MMSCFD (Million standard cubic feet per day), and that of the Gorae V-1 DST #2 is calculated to be 68.2 MMSCFD.