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Eltaleb, I. (University of Houston) | Rezaei, A. (University of Houston) | Siddiqui, F. (University of Houston) | Awad, M. M. (University of Houston) | Mansi, M. (University of Houston) | Dindoruk, B. (University of Houston) | Soliman, M. Y. (University of Houston)
The fracture injection fall-off test is a common technique for determining rock properties and fracture closure pressure. Conventional methods for analyzing DFIT are formulated based on the assumption of a vertical well and have shortcomings in horizontal wells drilled in ultra-low permeability reservoirs with potential multiple closures. In this study, an alternate technique using the signal processing approach is proposed. In the proposed method, we analyze the energy of the noise in the signal using a wavelet transform to identify the closure moment and pressure. We hypothesize that after the complete fracture closure moment, the noise in the recorded pressure will begin to vanish. To determine this closure moment, we decompose the pressure fall-off (signal) into multiple levels with different frequencies using the wavelet transform. Multiresolution wavelet decomposition breaks the (pressure) signal into high pass (noise) and low pass (approximation) components at various levels. The energy distribution plot is then constructed by plotting the energy of the high pass (noise) component versus the corresponding decomposition level.
Our results show that the noise energy reduces by several orders of magnitude at a specific time, which may identify the moment of fracture closure. Four field cases are analyzed using the proposed approach for demonstration. Also, we show an example where identifying the closure pressure using G-function is challenging, and our method still works reasonably well. Plots of the noise energy distribution versus time indicated multiple decreasing levels of energy. We also observed that the energy of the recorded noise in the signal could stay constant, or it can decrease gradually until the closure moment. In both cases, we observed that the signal energy drops to a minimum level at closure, and stays at that lowest level, thereby confirming our hypothesis. We also noted that the closure points that are found using this approach could happen before or after the closure from the conventional G-function method.
The main advantage of our proposed approach is that, unlike other physics-based techniques, it does not have any pre-assumption about the geometry of fracture or type of the well. It solely relies on the pressure signal that is recorded during the fall-off period. This advantage makes our approach unique since it is not limited to any specific formation, rock, or well type.
Awad, Mohamed M. (University of Houston) | Eltaleb, Ibrahim (University of Houston) | Mansi, Mohamed (University of Houston) | Rezaei, Ali (University of Houston) | Soliman, M. Y. (University of Houston) | Farouq-Ali, S.M. (University of Houston) | Dindoruk, Birol (Shell International Exploration and Production, Inc.)
Hydraulic fracturing rate and pressure data are interrelated signals and can be subjected to signal processing techniques. The energy of a range of frequency band can be computed from the wavelet transform, which is a powerful technique in signal processing. The signal energies of the rate (cause) and pressure (effect) are used to detect the fracture events in time, such as height growth, screen-out, and hydraulic fracture-natural fracture interactions. The objective of this study is to relate the signal energies obtained from the wavelet analysis to the real physical phenomena of fracturing propagation events during injection. With this technique, the fluid injection during hydraulic fracturing process may be monitored in real-time for early diagnosis and to take preventive steps during the fracturing treatment
Wavelet transform decomposes a signal into various levels of frequency components. The transform can be repeatedly applied to obtain a multiresolution analysis of the signal. Such repeated application of the wavelet transform yields the signal detail coefficients at each decomposition level, where each level represents a band of frequency. Using this approach, the pressure and rate signals from hydraulic fracturing were decomposed. Then, the pressure and rate signals energies at each frequency band were computed and compared to using energy density plots (EDP). Since wavelet transform preserves the time localization, all events on the EDP are obtained with respect to time. Finally, the energy distribution of the frequency bands for pressure and rate signals of different decomposition levels and using different wavelet types were studied with respect to time to distinguish between the rock-related and rate-related events in time. The moving reference point (MRP) technique is used to verify the results of our proposed approach.
Several field cases were analyzed in this study to show the robustness of the proposed approach. The events identified using the proposed approach were in good agreement with the established techniques in the literatures such as Nolte-Smith and moving reference point (MRP). The identified events were related to physical events happening during fracture propagation, such as height growth, the opening of the natural fracture system, screen-out, and proppant entering the formation. The main advantage of the developed methodology is its ability to identify fracturing events more accurately and earlier in time than other techniques.
The novelty of this research is detecting the fracturing events in time independently of the assumptions of fracture and well geometry. Ultimately, this technique helps to improve treatment designs and efficiency by analyzing fracture and formation behavior of the treatment and enhancing decision-making during execution, by providing real-time indications of fracture behavior.
Abstract Due to the shift from conventional reservoirs towards unconventional, ultra-low permeability reservoirs in the last decade, Diagnostic Fracture Injection Test (DFIT) has become one of the dominant and economically practical pressure transient tests. It is crucial to analyze and interpret DFIT data correctly to obtain essential fracture design and reservoir parameters. This study presents the application of wavelet analysis to DFIT falloff pressure data to determine fracture closure pressure and time, to ultimately improve the overall efficiency of hydraulic fracturing designs. In this study, DFIT pressure is treated as a non-stationary signal and analyzed by one of the signal processing techniques which is wavelet transformation. The purpose of signal analysis is to extract relevant information from a signal by transforming it. Firstly, the signal is transformed into wavelet domain by Discrete Wavelet Transformation (DWT) to calculate high-frequency wavelet coefficients (details), then change-point detection technique is applied to distinguish major changes within the coefficients trend to determine fracture closure pressure and time. DFIT pressure decline data from different wells were analyzed by wavelet transformation. Detail coefficient demonstrates different patterns depending on the formation analyzed and near wellbore activities. This is expected because wavelet analysis is sensitive to any physical changes within the system. From the amplitude changes of the coefficients, wavelet tool demonstrates the fracture closure as a continuing process. Because wavelet is sensitive to changes in the system, it detects the fracture closure unambiguously by amplitude change, as compared to slope changes in other conventional methodologies. A comparison with some of the most commonly used diagnostic techniques, conventional log-log diagnostic plot, square root time, G-function and its derivative analysis are also provided in this study. There have been several publications discussing various techniques analyzing DFIT pressure decline in unconventional formations and yet there is relatively high uncertainty in before-closure-analysis. However, this methodology is more sensitive to fundamental changes in the system, so application in detecting closure pressure and time decreases the uncertainty compared to other conventional tangential methodologies.
Unal, Ebru (University of Houston) | Rezaei, Ali (University of Houston) | Siddiqui, Fahd (University of Houston) | Likrama, Fatmir (Halliburton) | Soliman, M.. (University of Houston) | Dindoruk, Birol (Shell International Exploration and Production, Inc.)
abstract In the last decade, technical advancements have greatly improved the design and execution efficiency of well completions, leading to improved recovery from unconventional reservoirs. However, analyzing fracture diagnostic tests in unconventional plays are still challenging due to high uncertainty in predictive capabilities in the context of fracture dynamics during treatment. The main objective of this study is to identify fracture behavior during injection and pressure fall-off periods in hydraulic fracturing treatments and diagnostic fracture injection tests (DFIT), respectively. In this study, discrete wavelet transformation (DWT) was used to analyze real field injection and fall-off data in the wavelet domain. The analyzed data are from multi-stage hydraulic fracturing operations and DFIT in unconventional horizontal wells. DWT coefficients reveal very crucial information related to the nature of the events within recorded signals; they also reveal various patterns that are hard to recognize otherwise. The high-frequency components of the pressure and rate signals (detail coefficients) that are calculated by the wavelet transformation determine localization and separation of various events. We compared the identified events for injection and fall-off periods with moving reference point (MRP) and G-function analysis, respectively. The main advantage of our proposed approach is that it is based on real-time data and does not require any assumptions related to existing or created fractures. Also, it is very sensitive to physical changes in the system; thus, it reveals hidden information related to those changes. Consequently, the energy of detail coefficients represents several events at different frequencies. We used pseudo-frequency of wavelet coefficients as a diagnostic tool for an accurate comparison of fracture propagation and fracture closure events to determine similarities and differences between them. For example, the signal energy of detail coefficients from the wavelet transformation of hydraulic fracturing data demonstrates abrupt frequency changes during dilation or fracture height growth during fracture propagation. Therefore, we were able to identify those events by energy density analysis in both time and pseudo-frequency domains in an objective manner, which otherwise was not possible with conventional methodologies such as G- function derivative analysis. This paper details the successful methodology for effective implementation of a new fracture diagnostic technique for fracturing operations or DFITs in unconventional horizontal wells. This new fracture diagnostic method does not require any reservoir or fracture pre-assumptions; it mainly relies on the pressure behavior, which is a result of various events at different frequencies. Pressure fall-off behavior of a DFIT gives essential information related to closure event of the created mini-fracture. Identification of these events at different pseudo-frequency ranges improves the understanding of the dynamic fracture behavior also the characteristics of the reservoir. Unlike many other diagnostic techniques, this data-driven approach requires minimum input/data for analysis. This approach also lends itself to real-time application quite easily.
Xing, P. (Energy & Geoscience Institute, University of Utah) | Goncharov, A. (University of Utah) | Winkler, D. (Red Rocks, Inc.) | Rickard, B. (Geothermal Resource Group) | Barker, B. (University of Utah) | Finnila, A. (Golder Associates) | Ghassemi, A. (Reservoir Geomechanics and Seismicity Research Group, University of Oklahoma) | Podgorney, R. (Idaho National Laboratory) | Moore, J. (Energy & Geoscience Institute, University of Utah) | McLennan, J. (University of Utah)
ABSTRACT During April and early May 2019, injection testing was carried out in three granitic zones in a vertical well at the FORGE site near Milford Utah. The deepest zone was in an uncased openhole region that had also been treated in September 2017. Two cased and perforated intervals farther uphole were also evaluated. In a number of the injection cycles, flowback was implemented rather than shut-in, with the goal of finding an alternative to prolonged shut-in periods for inferring closure stress and formation permeability (transmissibility). The flowback data from the FORGE program involved a progressive increase in the choke size, or cyclic flowback/shut-in while pressure decreased. The flowback data are presented, and analyses are shown. The predictions are compared with equivalent injections that were strictly shut in. Closure signatures are considered, and after flow evaluations – for permeability (transmissibility) determination – are carried out. Flowback potentially has advantages over shut-in because of the reduced time to closure. 1. INTRODUCTION Enhanced Geothermal Systems (EGS) offer the potential to bring low-cost geothermal energy to locations that lack natural permeability through hydraulic stimulation (Moore et al., 2019). The U.S. Department of Energy selected a location near Milford, Utah, as the site for the Frontier Observatory for Research in Geothermal Energy (FORGE). The goal of the FORGE program is to develop the techniques required for creating, sustaining, and monitoring EGS reservoirs. In Sept 2017, an injection program was carried out in the openhole toe of Well 58-32 at the Utah FORGE site (see, for example, Balamir et al., 2018). Well 58-32 is approximately 7500 feet deep with 147 feet of open hole below the production casing shoe. A follow-on injection program was carried out in this same well in April and May, 2019. One of the aims of the 2019 testing program was to evaluate the repeatability of injection into the barefoot section along with the potential for pumping into cased and perforated zones farther uphole. Post-injection measurements were undertaken under shut-in conditions or while flowing back the well. The flowback measurements assessed using previously proposed technology as a substitute for unreasonably long shut-in periods as part of Diagnostic Fracture Injection Testing (DFIT).