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.)
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.
Unal, Ebru (University of Houston) | Siddiqui, Fahd (University of Houston) | Rezaei, Ali (University of Houston) | Eltaleb, Ibrahim (University of Houston) | Kabir, Shah (University of Houston) | Soliman, Mohamed Y. (University of Houston) | Dindoruk, Birol (Shell International Exploration and Production, Inc.)
Inter-well connectivity (IWC) is one of the most significant properties when evaluating the success of a waterflood. This connectivity has been obtained from various physics-based methods such as simulations, tracers and using heuristics and semi-analytical tools like capacitance-resistance model (CRM). Production and injection data are a key piece of information required to compute the IWC. In this study, we present a new method for estimating IWC using signal processing techniques on the wavelet transform of the injection and production rate data.
First, the injection and production rates are subjected to multiresolution analysis using the wavelet transform to determine the detail coefficients. The variance of the detail coefficients is then computed and is ready to be processed using various signal processing techniques. Signal processing techniques such as cross-correlation, time lag, Spearman correlation, and Kendal correlation are used to identify the level of relationship between the processed injection and production data in wavelet scale space. Based on the correlation coefficients, a new IWC link parameter is proposed for characterizing the IWC between well pairs. The IWC link parameters between well pairs are then plotted for visual representation.
We created several simulation models for multi-well systems, established water-flood patterns, and for randomly placed wells to establish the new IWC link parameter. The resulting injection and production rates were analyzed using the methodology above and the new IWC link parameter is established in terms of cross-correlation coefficient. We also performed several simulations for a heterogenous reservoir to compute and compare the accuracy of the new IWC link parameter. Finally, the methodology is subjected to real field waterflooding, and compared against the CRM results, which shows a good agreement. The visual representation gives new insight into whether the connectivity is being affected by the reservoir or from near wellbore events (such as changes in skin).
This study integrates signal processing techniques and waterflood IWCs. Novel use of wavelet transforms coupled with variance for processing the injection and production rate data is proposed. It must be emphasized that wavelet is used in this context for processing and not for smoothing or data compression. Ultimately, this method can be implemented as a real-time automated monitoring system. Moreover, the new IWC link parameter provides insights by identifying problematic IWC, well-completion issues, and high perm channels for taking timely operational decisions.
Gelman, Andriy (Schlumberger) | Maeso, Carlos (Schlumberger) | Godet, Vincent (Schlumberger) | Padin, Exequiel (Schlumberger) | Tarrius, Mathieu (Schlumberger) | Sun, Yong (Schlumberger) | Auchere, Jean-Christophe (Schlumberger) | A, Adrian (Schlumberger) | Wibowo, Vera (Schlumberger) | Shrivastava, Chandramani (Schlumberger)
This paper presents a novel borehole image compression algorithm for real-time (RT) logging while drilling (LWD). The compression scheme is designed to optimize the critical information required for RT decision making at low telemetry bandwidths. In the proposed algorithm we estimate the structure of the image (i.e. the amplitude and phase shift of the dip) and modify the encoding dictionary based on the features. The resulting dictionary resembles sinusoidal features, thus optimizing the reconstruction of bedding or other planar features in deviated wells. The dictionary is designed using a modified version of the 2D discrete wavelet transform (DWT). This approach has a low encoding complexity and supports the integration of directional information into the transform. Since feature estimation is a challenging step, we use a classifier to identify when directional information should be added to the transform or whether a conventional implementation is used. The algorithm has been implemented in both oil-and water-based mud LWD imager tools, where the low encoding complexity has facilitated the implementation in legacy tools with limited computation resources. We present field test results comparing the borehole images from RT and recorded mode (RM) data from one of the industry's first RT LWD resistivity images obtained from a well drilled using oil-based mud.
Sanguinito, Sean (National Energy Technology Laboratory) | Cvetic, Patricia (National Energy Technology Laboratory) | Goodman, Angela (National Energy Technology Laboratory) | Kutchko, Barbara (National Energy Technology Laboratory) | Natesakhawat, Sittichai (National Energy Technology Laboratory)
It is becoming increasingly important to expand the fundamental understanding of geochemical interactions between CO2, fluids, and shale. These interactions will significantly impact the processes of 1) storing CO2 in hydraulically fractured shale formations, 2) using CO2 as a fracturing agent, and 3) enhancing hydrocarbon recovery in shales via CO2 flooding. In this work, we use in-situ Fourier Transform infrared spectroscopy (FT-IR), feature relocation scanning electron microscopy (SEM), and surface area and pore size analysis using volumetric gas sorption and density function theory (DFT) methods to characterize and quantify the reactions that occur between CO2, fluids, and shale. Several shale samples from across the U.S. were analyzed including the Marcellus, Utica, and Eagle Ford Shales. CO2 will be injected into shale formations where it will interact with shale surfaces (i.e. clays, organic matter), in-situ fluids (i.e. natural brines), and previously injected fracturing fluid. Currently, it is assumed that dry supercritical CO2 does not interact with or have any impact on reservoir rocks or seals. Our suite of measurements show CO2 interaction with clay and kerogen components of the shale, reactivity and etching of carbonate, and changes in pore sizes at the meso- and micro-scale. Very few studies are taking into account the reactivity of CO2 and fluids in the reservoir. The reactions that occur between CO2, fluids, and the shale may alter petrophysical properties such as porosity and permeability which may alter flow pathways potentially impacting the storage permeance of CO2 and the effectiveness of CO2 to behave as a fracturing agent or to mobilize hydrocarbons.
With increasing awareness and concern of CO2 emissions and climate change, there has been a shift in research efforts to evaluate the potential of shales to be used as CO2 storage reservoirs and effective natural seals for CO2 or hydrocarbons (Orr, F.M., 2009a.; Orr, F.M., 2009b; Romanov et al., 2015; Levine et al., 2016, Bacon et al., 2015). Current research is underway to determine the fundamental understanding of geochemical interactions between CO2, fluids, and shale. Fluids, such as formation fluids and fracturing fluids, can react with the CO2 and shale interface to alter formation properties (Jun, Y et al., 2013; Dieterich et al., 2016). This geochemical alteration of shale has been reported to directly affect porosity, permeability, flow paths, and integrity of the wellbore, seal, and formation (DePaolo and Cole, 2013). Additionally, the storage temperature and pressure conditions and the composition and chemistry of brine solution and hydraulic fracturing fluid have an impact on the geochemical alteration of the shale (specifically dissolution).
Lee, Wei Yi (Centre of Subsurface Seismic Imaging, CSI, Universiti Teknologi PETRONAS) | Hamidi, Rosita (Centre of Subsurface Seismic Imaging, CSI, Universiti Teknologi PETRONAS) | Ghosh, Deva (Centre of Subsurface Seismic Imaging, CSI, Universiti Teknologi PETRONAS) | Musa, Mohd Hafiz (Centre of Subsurface Seismic Imaging, CSI, Universiti Teknologi PETRONAS)
Noise is the unwanted energy in a seismic trace opposed to the signals corresponding to reflected energy from the subsurface features. Since it can overlap with the main signals' energy and conceal the geological information, noise attenuation is one of the most important steps in seismic data processing. The most common method is frequency filtering. However, due to its limitations on separating the noise from signals, this method usually results in hurting the signal. Hence, it is important to develop an alternative method that can attenuate the noise without affecting the signal. Filters based on time-frequency analysis of the data can have a better separation of the noise from signal as they maintain the time localization of events while presenting their frequency content simultaneously. One of the recent approaches to time-frequency analysis of signals is the Empirical Wavelet Transform (EWT) which provides adaptive wavelet filter bank for signal analysis. In this paper, a filter is designed based on EWT for random noise attenuation and is applied on both synthetic and real data.
The present paper is concerned with an experimental study of the acoustic signature of phase inversion in an oil-water mixture system. The system studied was used to correlate the process of phase inversion with the acoustic field generated during the two fluid mixing. The experimental results revealed that the relation between the acoustic fields produced by a water continuous dispersion and the phase inversion has a clear and different signature from an oil-continuous system using a batch mixing system. This dynamical characteristic of the phase inversion phenomenon could be of use in practical systems to detect phase inversion when it occurs based on the acoustic field measured in the subject process.
In this paper we focus on electrical-submersible-pump (ESP) failure caused by scale buildup. Weak fluctuations recorded in the motor current signals several weeks before a failure indicate a change in the motor load. Advanced signal analysis of the motor current data reveals the presence of a dynamic characteristic in the ESP signal during rapid scale buildup in the pump stages. On the basis of the raw data from the motor current draw, a dynamic cascade can be identified in the current marked with the superimposition of several characteristic frequencies added over time that develop into a chaotic trend. Our analysis was conducted with different signal-processing tools, such as Fourier transform, wavelet transform, and chaotic attractors, which described the nature of the scale signature in the current logs. This analysis was the first step toward developing a real-time diagnostic tool for predicting ESP failures.
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.
Wei Chen, Yu Zhou, and Weigang Yu, Soochow University, and Leilei Yang, China University of Petroleum, Beijing Summary In this study, shale samples were heated under inert and noninert environments to increase the permeability of the shale. It was found that pore diameters increased under all the gas environments. Pore diameters increased more significantly under air environment compared with other gas conditions. However, the diameters of the shale particles remained almost constant during combustion. Moreover, gases emitted from the shale during the combustion and pyrolysis process were investigated using thermogravimetric analysis coupled to Fourier-transform infrared spectroscopy (TGA-FTIR). Finally, scanning electron microscopy (SEM) images showed larger pores on the surfaces of the combusted and pyrolyzed shale samples.
Goraya, Yassar (Adnoc Offshore) | Nair, Rajeev Nair (Fugro) | AL-Neaimi, Ahmed Khalifa (Adnoc Offshore) | AL-Felasi, Ali (Adnoc Offshore) | Kleef, Franciscus Johannes (Adnoc Offshore) | Al-Dhafari, Bader (Adnoc Offshore) | El-Sayed, Mohamed Abdul-Khalek (Adnoc Offshore) | Akram, Fazeel (Adnoc Offshore) | AL-Hosani, Ibrahim Ali (Adnoc Offshore)
During a routine tower maintenance visit, gas bubbles were observed at sea bed. The challenge now was to identify the source of the gas leak and identify areas where gas had accumulated. The observed gas seep, escaped from the seabed to the water column and was in the vicinity of the TWR-2 platform as confirmed during a diving survey. A geophysical survey was initiated to understand if gas had accumulated in the subsurface and whether it was safe to approach the site with a rig to kill the well.