Well-test Data Research Articles

A Deep-Learning-Based Oil-Well-Testing Stage Interpretation Model Integrating Multi-Feature Extraction Methods

The interpretation of well-testing data is a key means of decision-making support for oil and gas field development. However, conventional processing methods have many problems, such as the stochastic nature of the data, feature redundancies, the randomness of the initial weights or thresholds, and fluctuations in the generalization ability with slight changes in the network parameters. These result in a poor ability to characterize data features and a low generalization ability of the interpretation models. We propose a new integrated well-testing interpretation model based on a multi-feature extraction method and deep mutual information classifiers (MFE-DMIC). This model can avoid the low model classification accuracy caused by the simple training structures, lacking of redundancy elimination, and the non-optimal classifier configuration parameters. First, we obtained the initial features according to four classical feature extraction methods. Then, we eliminated feature redundancies using a deep belief network and united the maximum information coefficient method to achieve feature purification. Finally, we calculated the interpretation results using a hybrid particle swarm optimization–support vector machine classification system. We used 572 well-testing field samples, including five working stages, for model training and testing. The results show that the MFE-DMIC model had the highest total stage classification accuracy of 98.18% as well as the least of features (nine) compared with the classical feature extraction and classification methods and their combinations. The proposed model can reduce the efforts of oil analysts and allow accurate labeling of samples to be predicted.

Integration of Pressure Transient Data into Reservoir Models Using the Fast Marching Method

SummaryCalibration of reservoir model properties by integration of well-test data remains an important research topic. Well-test data have been recognized as an effective tool to describe transient flow behavior in petroleum reservoirs. It is also closely related to the drainage volume of the well and the pressure-front propagation in the subsurface. Traditional analytic means of estimating reservoir permeability relies on an interpretation of the diagnostic plot of the well pressure and production data, which usually leads to a bulk average estimation of the reservoir permeability. When more detailed characterization is needed, a forward model that is sensitive to the reservoir heterogeneity needs to be established, and a numerical inversion technique is required.We use the concept of the diffusive time of flight (DTOF) to formulate an asymptotic solution of the diffusivity equation that describes transient flow behavior in heterogeneous petroleum reservoirs. The DTOF is obtained from the solution of the Eikonal equation using the fast marching method (FMM). It can be used as a spatial coordinate that reduces the 3D diffusivity equation to an equivalent 1D formulation. We investigate the drainage-volume evolution as a function of time in terms of the DTOF. The drainage volume might be directly related to the well-test derivative, which can be used in an inversion calculation to calibrate reservoir model parameters.The analytic sensitivity coefficients of the well-test derivative with respect to reservoir permeability are derived and incorporated into an objective function to perform model calibration. The key to formulating the sensitivity coefficients is to use the functional derivative of the Eikonal equation to derive the analytic sensitivity of the DTOF to reservoir permeability. Its solution is implemented by tracking the characteristic trajectory of the local Eikonal solver within the FMM. The major advantage of calculating the sensitivity coefficients using the FMM is its significant computational efficiency during the iterative inversion process.This inverse-modeling approach is tested on a 2D synthetic heterogeneous reservoir model and then applied to the 3D Brugge Field, where a single well with constant flow rate is simulated. The well-test derivative is shown to be inversely proportional to the drainage volume and is treated as the objective function for inversion. With an additional constraint to honor the prior model, our inverse-modeling approach will adjust the reservoir model to obtain permeability as a function of distance from the well within the drainage volume. It provides a modification of reservoir permeability both within and beyond the depth of investigation (DOI).

Vibration-based Processing and Classification Method for Oil Well-testing Data from Downhole Pressure Gauges

During petroleum exploration and exploitation, the oil well-testing data collected by pressure gauges are used for monitoring the well condition and recording the reservoir performance. However, due to the large number of the collected data, the classification of this large volume of data requires a previous processing for the removal of noise and outliers. It is impractical to partition and process these data manually. Vibration-based features reflect geological properties and offer a promising option to fulfil such requirements. Based on the 75 on-site measured samples, the time-frequency-domain features are extracted and the classification performance of three classical classifiers are investigated. Then the downhole data processing and classification method is present by analysing the cross interaction of different types of data features and different classification mechanism. Several feature combinations are tested to establish a processing flow that can efficiently remove the noise and preserve the shape of curves, high signal to noise ratio rates, with minimum absolute errors. The results show that optimal multi-feature combination can achieve the highest working stage identification rate of 72%, the parameters optimized support vector machine can achieve the better classification performance than other listed classifiers. This paper provides a theoretical study for the data denoising and processing to enhance the working stage classification accuracy.

Elucidating sources to aridland Dalhousie Springs in the Great Artesian Basin (Australia) to inform conservation

Dalhousie Springs is the largest spring complex in the western Great Artesian Basin (GAB), Australia. Aridland springs like Dalhousie provide the only aquatic habitats in regions lacking surface water and are globally threatened by unsustainable groundwater development. Groundwater use in the more densely populated eastern GAB historically was higher than that in the western GAB, where groundwater is primarily used for ranching; however, economically important mineral and energy industries have increased groundwater use. Throughout the western GAB, groundwater development has reduced spring discharge and artesian head. Of concern are potential impacts on spring discharge from future pumping; thus, an understanding of groundwater sources to springs is needed to develop effective groundwater management strategies that maintain spring flow. The generally accepted hydrogeologic model suggests Dalhousie Springs discharge is entirely composed of Jurassic-Cretaceous aquifer contributions; however, this study improves understanding of Dalhousie Springs by integrating new hydrogeologic and hydrochemical data with historic, previously unpublished petroleum exploration well-test data. A thermal model is used to estimate potential aquifer source depths of 270–802 m. 87Sr/86Sr > 0.715 suggests water–rock interaction with radiogenic basement and the importance of faults for vertical fluid transfer across multiple aquifers. Results show that Dalhousie Springs discharge is sourced by the previously unreported Permian Crown Point Formation and the Jurassic-Cretaceous aquifer. Mitigating effects of future groundwater development on Dalhousie Springs requires managing groundwater from Jurassic-Cretaceous and Permian aquifers to preserve near-spring potentiometric surfaces. Expanded multiple-environmental-tracer monitoring could be used to further refine groundwater sources to Dalhousie Springs.

Application of the long short-term memory networks for well-testing data interpretation in tight reservoirs

Conventional interpretation of the well-testing data cannot effectively detect the real formation response from the noise to characterize the reservoir. Machine learning techniques provide a new data interpretation approach to discover the relationships between production rates and pressure responses of the well. In this study, the long short-term memory networks (LSTMNs) are explored to analyze the field permanent downhole gauge (PDG) data for better reservoir characterization and modelling. Unlike the conventional recurrent neural networks (RNNs), LSTMNs are designed to learn the long-term dependencies among the sequential data sets. More specifically, an approximate analytical model is firstly proposed to describe the flow rate and pressure behavior in a tight reservoir with natural fractures. The synthetic flow rate and pressure data with noise generated by the analytical model are then used to train the LSTMNs. Prediction accuracy of the LSTMNs is first validated by using the field data sets collected from Montney Formation, and their applicability for reservoirs with different types of boundaries are tested by the synthetic flow rate and pressure data sets generated from the analytical models. The field cases have proved that this data mining technique is able to capture the well shut-in operations in Montney liquid-rich tight reservoirs, where the well bottomhole pressure can be accurately predicted when feeding the model with both gas rate and condensate rate. In addition, the LSTMNs are able to learn the pressure behavior from the noisy data sets and do not require a denoising procedure when predicting the pressure response for a given flow rate. In summary, this work first uses the LSTMNs to interpret well-testing data in naturally fractured tight reservoirs. The LSTMNs not only shows a great tolerance to the noise of datasets, but also can capture the pressure responses characterized by well shut-in, boundary effects, and stress-sensitive fractures. It is proved that this data-driven model can discover the patterns and relationships between the flow rate and pressure through the data mining process, while not requiring the prior knowledge of physical model or mathematical assumptions.

After-Closure Analysis of DFITs Drives Design of Hydraulic-Fracturing Programs

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE-191437-18IHFT-MS, “ACA Practical Considerations: When Is It Accurate and How Should It Be Used To Improve Reservoir Stimulation,” by O.A. Ishteiwy, SPE, M. Jaboob, and G. Turk, BP; S. Dwi-Kurniadi, SPE, Schlumberger; A. Al-Shueili, SPE, A. Al-Manji, and P. Smith, BP, prepared for the 2018 SPE International Hydraulic Fracturing Technology Conference and Exhibition, Muscat, Oman, 16–18 October. The paper has not been peer reviewed. The use of diagnostic fracture injection tests (DFITs) for prefracture investigation has become routine in the oil field, particularly for understanding reservoir properties and subsequently optimizing hydraulic-fracture design. A key component of an effective DFIT is an after-closure analysis (ACA) to assess the transmissibility of the formation and allow for effective design. This paper describes a DFIT-analysis program and the suitability of the results from ACAs for use in hydraulic-fracture design. Introduction The Khazzan field is being developed currently and includes multiple gas-bearing formations. The primary development reservoir is the Barik sandstone, which is characterized by permeabilities on the order of 0.1 to 1 md. An additional reservoir under development is the Amin formation, which lies deeper than the Barik and is perhaps more unconventional in nature, with estimated permeabilities an order of magnitude lower than the Barik formation. Both reservoirs require hydraulic fracturing to produce at economically attractive rates and, as such, carry the same sort of challenges to reservoir understanding inherent to all unconventional plays. This was recognized in advance of the appraisal program, and an approach was taken to address these challenges in a more-holistic fashion, encompassing a full suite of data gathering, including surveillance and well testing. One of the key tools used was DFIT along with associated ACA of the decline to determine reservoir properties. During the appraisal phase, significant rigor was aimed at ensuring high-quality data would be recorded and that an appropriate amount of time would be allocated to monitoring pressure declines to enable valid interpretations. This resulted in the ability to draw a good correlation between data gathered from the ACA operations and data collected from post-fracturing well-test data. Methods and Process Stimulation and Testing Sequence. The approach taken to stimulate and test the wells in Khazzan was to use a dedicated well-test unit. The overall sequence was as follows: Rig up well-test package Displace kill fluid and clean out with coiled tubing Perforate the target interval Rig up a tree-saver Perform DFIT and monitor pressure decline Perform main fracturing Establish post-main-fracturing-treatment pressure-decline period for fracture closure Rig down the tree-saver Clean out underdisplaced proppant with coiled tubing Flow the well back for cleanup and testing Perform a drift run with slickline to confirm hold-up depth Rig down equipment and handover well to operations

A fractally fractional diffusion model of composite dual-porosity for multiple fractured horizontal wells with stimulated reservoir volume in tight gas reservoirs

Based on fractal theory (FT) and fractional calculus (FC), a new fractally fractional diffusion model (FFDM) of composite dual-porosity has been developed to evaluate performance of multiple fractured horizontal wells (MFHWs) with stimulated reservoir volume (SRV) in tight gas reservoirs (TGRs). More specifically, FT is used to characterize the complex and heterogeneous fracture network (FN) both inside and outside of SRV, while anomalous behavior of diffusion processes both inside and outside of SRV is quantified by applying the temporal fractional derivatives. The FFDM is then solved by the Laplace transformation, line source function, the numerical discrete method, and superposition principle. The transient pressure responses are then inversely converted from Laplace domain into real time domain with the Stehfest algorithm, and the FFDM is also validated, and type curves are generated as well. Flow stages are subsequently identified together with analysis on characteristics of the type curves, especially the anomalous features different with those generated from the conventional Euclidean model. Sensitivity analyses of some related parameters have also been discussed as well. And the FFDM is then also matched with the real field well-testing data of a MFHW with SRV in a TGR. The proposed FFDM provides a new understanding of the performance of MFHWs with SRV in TGRs, which can be used to interpret the field pressure data more accurately and appropriately.

A machine-learning approach for noise reduction in parameter estimation inverse problems, applied to characterization of oil reservoirs

Reservoir characterization is an inverse problem where parameter values associated to the properties of the porous media, are estimated. In this problem, the pressure data and its log-derivative curves are used in the fitting process. However, the numerical differentiation needed to generate the log-derivative curve, is an ill-posed problem where the noise in the data can be largely propagated. This noise can produce several spurious local minima in the objective function and prevents to get a precise approximation to the parameters. Therefore, an efficient noise reduction method is required to achieve the desired accuracy in the parameter values.In this work, we explore three noise reduction methods by applying them to well-test data. These methods are based on multi-step finite differences and splines, but using a machine-learning approach.In addition, a new method is proposed which produce an optimal curve which is a linear combination of multiple data curves, that are approximations to the solution of the inverse problem.We show how these methods which use information from the forward model are more effcient for noise reduction. The proposed methods can be used in many inverse problems where there is a mathematical model meant to describe the measured data.

Analysing the limitations of the dual-porosity response during well tests in naturally fractured reservoirs

Geological reservoirs can be extensively fractured but the well-test signatures observed in the wells may not show a pressure transient response that is representative of naturally fractured reservoirs (NFRs): for example, one that indicates two distinct pore systems (i.e. the mobile fractures and immobile matrix). Yet, the production behaviour may still be influenced by these fractures. To improve the exploitation of hydrocarbons from NFRs, we therefore need to improve our understanding of fluid-flow behaviour in fractures. Multiple techniques are used to detect the presence and extent of fractures in a reservoir. Of particular interest to this work is the analysis of well-test data in order to interpret the flow behaviour in an NFR. An important concept for interpreting well-test data from an NFR is the theory of dual-porosity model. However, several studies pointed out that the dual-porosity model may not be appropriate for interpreting well tests from all fractured reservoirs. This paper therefore uses geological well-testing insights to explore the limitations of the characteristic flow behaviour inherent to the dual-porosity model in interpreting well-test data from Type II and III NFRs of Nelson's classification. To achieve this, we apply a geoengineering workflow with discrete fracture matrix (DFM) modelling techniques and unstructured-grid reservoir simulations to generate synthetic pressure transient data in both idealized fracture geometries and real fracture networks mapped in an outcrop of the Jandaira Formation. We also present key reservoir features that account for the classic V-shape pressure derivative response in NFRs. These include effects of fracture skin, a very tight matrix permeability and wells intersecting a minor, unconnected fracture close to a large fracture or fracture network. Our findings apply to both connected and disconnected fracture networks.

Applying orthogonal collocation for rapid and reliable solutions of transient flow in naturally fractured reservoirs

Naturally fractured reservoirs (NFR) contain a significant portion of the worldwide oil and gas reserves. Pressure transient analysis is the most well-established methodology for characterizing these heterogeneous reservoirs based upon well-test data. Pressure transient responses (PTR) of oil and gas reservoirs to various production/injection scenarios is widely used for forecasting and optimizing their production performance and ultimate resource recoveries.In this study, orthogonal collocation (OC) which is a straightforward and very efficient approximate approach for solving differential equations is employed as a novel approach to simulate the PTR of NFRs. The governing equations describe fluid flow in NFR for pseudo-steady state conditions. Constant production rate from a wellbore and circular no-flow are employed as an inner and outer boundary conditions, respectively.The results of OC are verified with exact analytical solutions using the Laplace transform. Thereafter, the effects of three parameters (storativity ratio, interporosity flow coefficient, and radial distance of the outer boundary from the wellbore) are studied using pressure-derivative plots. Employing the superposition theorem, the pressure response of NFR to the multi-rate production from the wellbore is calculated. The results indicate that this approximate approach can predict the pressure transient response of NFRs with an acceptable accuracy, i.e., absolute average relative deviation (AARD) of 0.16%. The equations can be adapted to deal with other flow conditions, boundary conditions and fracture stimulated reservoirs. The proposed method provides a straightforward, faster and more accurate solution in comparison with other methods commonly used. As the OC method is simpler to execute than analytical solutions (e.g. Laplace Transform), it is easier to modify and expand for addressing more complex reservoir scenarios. Likewise, the numerical methods (e.g. finite difference and finite elements) commonly applied to model fractured reservoirs involve significantly more time-consuming calculations, making them more cumbersome to use in reservoir simulations than the proposed orthogonal collocation method.

Characterization of unconventional reservoirs and continuous accumulations of natural gas in the Carboniferous-Permian strata, mid-eastern Qinshui basin, China

Deep coalbed methane, with its associated shale gas and tight sandstone gas resources in coal-bearing strata has been an area of research focus in China. Identifying gas accumulation potential and reservoir characteristics is therefore of great importance in understanding the co-exploration potential of unconventional gases. Accordingly, comprehensive experimental results, and incorporation with drilling and well-testing data in the mid-eastern Qinshui basin were used and analysed in this paper for the reservoir study. Tests on organic geochemistry, gas storage capacities, petrology and petrophysics, and gas inclusions were performed on massive core samples in the Carboniferous-Permian (C3t-P1s) strata.It is found that coal seams in this area are buried deeply, with the depth (mainly over 1000 m) increasing from east to west, and the gas contents of the coal generally increasing to depths of 400–1600 m, as a result of low-level temperature increment and high reservoir pressure. Dark shales and sandstones are widely distributed, with thicknesses of 12.2–65.9 m and 5–35 m in the Shanxi Formation and 40.5–131.7 m and 5–50 m in the Taiyuan Formation, respectively. Shales are rich in over-mature type II and type III organic matter and are mainly composed of clays (47%) and quartz (36%). Sandstones are mostly tight with low porosity (lower than 5% on average) and permeability (<1 mD) in the C3t-P1s strata, among which the Xiashihezi Formation shows relatively higher porosity and permeability than the underlying Shanxi and Taiyuan Formations. After the sedimentation of C3t-P1s coal-bearing strata, two main gas generation episodes and one continuous gas expulsion process occurred during the Late Triassic to Early Cretaceous. High mature source rocks (i.e., coals and shales) and widely distributed reservoirs (i.e., coals, shales, and sandstones) offer good associations for continuous gas accumulation, in which two types of gas accumulation patterns existed in the coal-bearing strata. The whole coal-bearing system in this area is favourable for co-exploration of unconventional natural gases in different gas reservoirs.

A stochastic well-test analysis on transient pressure data using iterative ensemble Kalman filter

Accurate estimation of the reservoir parameters is crucial to predict the future reservoir behavior. Well testing is a dynamic method used to estimate the petro-physical reservoir parameters through imposing a rate disturbance at the wellhead and recording the pressure data in the wellbore. However, an accurate estimation of the reservoir parameters from well-test data is vulnerable to the noise at the recorded data, the non-uniqueness of the obtained match, and the accuracy of the optimization algorithm. Different stochastic optimization methods have been applied to this address problem in the literature. In this study, we apply the recently developed iterative ensemble Kalman filter in the context of well-test analysis to infer reservoir parameters from the noisy recorded data. Since the introduction of the ensemble Kalman filter (EnKF) by Evensen in 1994 as a novel method for data assimilation, it has had enormous impact in many application domains because of its robustness and ease of implementation, and numerical evidence of its accuracy. While the objective of the standard EnKF approaches is to approximate the statistical properties of geological parameters conditioned to observation, via an ensemble, the objective of the iterative ensemble Kalman methods is to approximate the solution of inverse problems using a deterministic derivative-free iterative scheme. We conducted three case studies of the application of the iterative ensemble Kalman methods for a well-test analysis of a homogenous reservoir model, a dual-porosity heterogeneous system, and a faulted discontinuous reservoir. We demonstrated that the convergence occurs very rapidly almost at the first iterations contrary to the well-known particle swarm optimization algorithm. The maximum relative error for the simulated cases is below 15%, which belongs to the skin factor. Low relative error, narrowed uncertainty range over time, and excellent graphical match obtained between the simulated derivative data and the generated curve by using the iterative EnKF verify the robustness of the developed algorithm even in dealing with complex heterogeneous models.

A New Method for Production–Data Analysis and Well Testing by Use of Superposition Rate

Summary This research presents a new method to analyze production- and well-test data: the superposition rate. The method was developed from the well-accepted superposition principle. It is presented in a generalized form and is applicable to data in transient flow (including radial, linear, and bilinear), as well as in boundary-dominated flow (BDF). The superposition-rate method is validated by synthetic data generated from reservoir modeling. Moreover, a practical work flow of implementing the superposition rate in production-data and well-test analysis is presented. Finally, real-field examples are used to demonstrate the practicality of superposition rate. A comparison between the superposition-rate and superposition-time methods is presented. The superposition rate shows advantages over the superposition time. A key improvement of the superposition rate in quality diagnostics and data analysis is that it does not modify time scale. Consequently, the superposition rate keeps all production data in the sequence of their occurrence.

A scaled perspective on permeability: How to get the best from what we have

The suppression and equivalence of permeability and thickness of transmissive reservoir intervals are managed by integrating data measured at different scales. Essential requirements are estimates of effective permeability from core, logs and well tests for comparison over net reservoir intervals at key wells. The starting point is a quality-assured determination of interval transmissibility from well testing. The interval thickness is refined to effective thickness through the scale-compatible application of dynamically-conditioned net-reservoir discriminators, a process that allows effective permeability to be determined at the well-test scale. Algorithms for transforming core absolute permeability to core effective permeability form the basis for comparisons of laboratory and well-test data provided that appropriate core compaction corrections have been made with data partitioning as needed. Where core sampling is comprehensive and representative over the perforated interval, the comparison with well-tests can be made directly. Otherwise core-calibrated log data constitute an essential intermediary. In the absence of formation-damage effects, the reconciliation of petrophysically- and dynamically-derived effective permeabilities over a tested interval is diagnostic of uniform reservoir character. Beyond data shortfalls, impediments to reconciliation include formation anisotropy exacerbated by natural fractures and relatively high permeability conduits possibly in the form of “super-k” layers. A deterministic workflow for achieving reconciliation is substantiated by reference to field examples, which collectively reveal further opportunities for improved permeability characterization in integrated reservoir studies.

Determination of the fracture pressure from CO2 injection time-series datasets

Successful operation of CO2 storage projects requires careful management of injection pressure to ensure that the integrity of the cap rock and other containment units is not compromised by mechanical failure. Injection of CO2 will lead to some increase in the pore pressure of the target storage formation and the project design will include some expected range of acceptable pressure variations required to achieve sufficient injectivity and to avoid loss of formation integrity. Accurate determination of the formation fracture pressures is a key factor controlling the maximum allowable injection pressure during operations. In this study we review the three main approaches for determining fracture pressure: (a) theoretical methods, (b) use of formation well-test data and (c) analysis of injection data during injection. While theoretical methods are valuable for early planning of projects, predictions based on formation well-test data are generally used immediately prior to and during injection operations. Using example site data, we show how careful analysis of wellhead pressure and rate data during injection operations may be used to optimize the injection plan using the actual in situ conditions in the rock formation around the injection well and for the specific properties of the injection fluid. Using injection pressure time-series datasets over an extended period of time allows improved accuracy in the estimation of the fracture pressure limits and an appreciation of their variation over a larger volume of the formation than can be estimated from well tests.

Uncertainty Quantification for History-Matching Problems

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 181611, “Uncertainty Quantification for History-Matching Problems With Multiple Best Matches Using a Distributed Gauss-Newton Method,” by Guohua Gao, SPE, Jeroen C. Vink, SPE, Chaohui Chen, SPE, Mohammadali Tarrahi, SPE, and Yaakoub El Khamra, Shell, prepared for the 2016 SPE Annual Technical Conference and Exhibition, Dubai, 26–28 September. The paper has not been peer reviewed. It is critically important for decision making and an extremely challenging task to quantify the uncertainty of model parameters and production forecasts properly after conditioning to production data. This paper presents a novel approach to generate approximate conditional realizations using the distributed Gauss-Newton (DGN) method together with a multiple local Gaussian approximation technique. Results are compared with those obtained from other approaches, such as randomized maximum likelihood (RML), ensemble Kalman filter (EnKF), and Markov-chain Monte Carlo (MCMC). Introduction It is well-known that oil- and gasfield development is a high-risk venture. Uncertainties originating from geological models (e.g., structure, stratigraphy, channels, and geobodies) are coupled with uncertainties of reservoir models (e.g., distribution of permeability and porosity in the reservoir) and uncertainties of economic parameters (e.g., oil and gas prices and costs associated with drilling and other operations). It is critically important to properly quantify the uncertainty of such parameters and their effect on production forecasts and economic evaluations. Recently, multiobjective-optimization techniques have been developed to maximize expectations of some economic indicators (e.g., net present value) and, at the same time, to minimize associated uncertainty or risk. Because of limited access to the subsurface reservoir (e.g., it is impossible to measure the permeability and porosity at the location of each gridblock of a simulation model), reservoir properties have quite large uncertainties. In a greenfield, before starting production, available data are limited to static data or hard data such as core data, well-log data, well-testing data, and sometimes seismic data. Hence, it is necessary to generate multiple realizations of structural and petrophysical models with limited information, on the basis of prior estimates of the relative probability of each realization. Then, uncertainty in reservoir performance or production forecasting can be quantified (e.g., with design of experiment or with other techniques). Generally, production profiles generated by running reservoir-simulation models with these prior realizations or unconditional realizations do not honor production data. Uncertain model parameters, such as permeability and porosity, potentially in each gridblock, have to be tuned so that the modified reservoir models are able to generate production profiles that are compatible with production data (e.g., by use of assisted history matching).

Technology Focus: Well Testing (February 2017)

Technology Focus 2016 was another slow year in the oil industry as the price of oil remained low. Despite the ongoing downturn, the 18th meeting of the Win Cubed Well Test Net-work (WTN) was hosted by Tetra Technologies in Houston in April. There were 37 attendees present plus 11 others who participated by telephone. Delegates represented a broad spectrum of operators and service companies, as well as a few independent consultants. Fourteen presentations covered success stories and innovations developed and implemented in the industry. During the 2-day meeting, an enthusiastic and open-minded participation was commonplace. A significant portion of participants contributed to the discussion and, on the basis of experience, helped build on the presentations to ensure that relevant information was shared with the group. Plans are already under way for the WTN 2017 meeting in Houston. For those interested, further information about the meeting as well as steps to join the network can be found at www.wincubed.co.uk. Continued enthusiasm in the well-testing segment of the oil industry is also apparent. Even though there was a smaller number of articles and presentations among various conferences related to well testing during 2016, there are articles that contribute significantly not only for the experts but also for the upcoming generation. The papers for this issue of JPT were selected to cover field applications as well as value added by consultants and academics. As difficult as it is to narrow down the list to three manuscripts, I have confidence that, in the end, these three manuscripts show insightful use of well-test data. The additional-reading articles selected offer many insights that are of value to the practitioner. Most certainly, the interested reader should seek further articles on this topic in the OnePetro library. Recommended additional reading at OnePetro: www.onepetro.org. SPE 177785 Successful Delivery of Downhole Measurements From Abu Dhabi Giant Offshore Field by Daniel Newton, Zakum Development Company, et al. SPE 179008 Applied Buildup-Well-Test Interpretation: Field Testing of Conventional and Deconvolution Methods by Omar Rosario, PDVSA, et al. SPE 181352 Consistent Integration of Drillstem-Test Data Into Reservoir Models on a Giant Field Offshore Norway by Jon Sætrom, Resoptima, et al. SPE 181546 Temperature Transients Affect Reservoir-Pressure Estimation During Well Tests—Case Study and Model by Pierre-David Maizeret, Schlumberger, et al.

Исследование особенностей выработки запасов трещинно-поровых коллекторов с использованием данных гидродинамических исследований скважин

Исследование особенностей выработки запасов трещинно-поровых коллекторов с использованием данных гидродинамических исследований скважин

Productivity Analysis Method of Abnormal High-Pressure Gas Reservoir in Ying-Qiong Basin

Ying-Qiong Basin in the west of South China Sea contains plenty of abnormal high-pressure gas reservoirs, whose stress sensitivity is crucial for well productivity. To explore the influence of stress sensitivity on production, the variable outlet back pressure stress sensitivity experiments were applied to test core sample permeability under different burden pressure and obtain the relational expression of power function of core stress sensitivity. Afterwards, new productivity equation is deduced in consideration of reservoir stress sensitivity, and the affection of stress sensitivity on production is analyzed. The result demonstrates close link between stress sensitivity and productivity, since single well production decreases dramatically when reservoir stress sensitivity has been taken into account. This research is constructive for well-testing data interpretation in stress sensitive gas reservoirs.

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

Ilk 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 improved deconvolution algorithm have good agreement with the ones by Von Schroeter et al.’s deconvolution algorithm and by Levitan et al.’s deconvolution algorithm as a whole; and the feature of typical log-log curves of the wellbore pressure drop and the wellbore pressure derivative corresponding to the improved algorithm is very close to the one of typical log-log curves calculated directly from the wellbore pressure data in the well shut-in period. In addition, through many numerical experimental tests, it is also concluded that as the quantity of data largely increases, the improved Ilk et al.'s deconvolution algorithm exhibits the big advantage in fast computational speed over von Schroeter et al.’s algorithm and Levitan et al.’s algorithm.