Microseismic Monitoring and Analysis with Cutting-Edge Technology: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Jason Zhu and Version 1 by Daniel Stephen Wamriew.

Microseismic monitoring is a useful enabler for reservoir characterization without which the information on the effects of reservoir operations such as hydraulic fracturing, enhanced oil recovery, carbon dioxide, or natural gas geological storage would be obscured. The global energy demand is projected to increase. To meet the increasing energy demand requires new technologies to exploit unconventional reserves. Similarly, calls for climate actions such as carbon geosequestration, hydrogen generation, and geological hydrogen storage will require an improvement in reservoir characterization methods.

  • microseismic monitoring
  • deep learning
  • distributed acoustic sensors

1. Introduction

The global energy demand is projected to increase. To meet the increasing energy demand requires new technologies to exploit unconventional reserves. Similarly, calls for climate actions such as carbon geosequestration, hydrogen generation, and geological hydrogen storage will require an improvement in reservoir characterization methods [1,2,3,4][1][2][3][4]. Seismology remains one of the most relevant instruments in reservoir characterization. The importance of seismology in reservoir characterization is extensively covered in the literature [5,6,7,8,9][5][6][7][8][9].

2. Microseismology in Reservoir Characterization

Estimating the petrophysical properties of reservoirs is integral to reserve and resource estimation. While petrophysical measurements from well logs adequately evaluate the reservoir properties, the correlation with seismic data better validates the measured properties and reduces errors [11,12][10][11]. The availability of continuous microseismic data helps uspeople to understand the geomechanical and petrophysical changes in the reservoir. Furthermore, the real-time data and analysis of such petrophysical changes could contribute to the initial screening process of feasible enhanced oil recovery methods [13][12].
Hydraulic fracturing is one of the most popular techniques for enhanced oil recovery and geothermal production. It involves the injection of huge volumes of a special liquid under high pressure into the geological formations to create new fractures and open up existing ones. Since this process has a direct mechanical effect on the geological formations, there is a potential to induce microseismic events locally. Foulger [14][13] indicated that about 21 earthquakes were induced due to hydraulic fracturing. Similar geological exploitation such as geothermal energy extraction has also been reported to have an increased microseismic rate [15,16][14][15].
Numerous processes involving the injection of gases have been at the forefront of studies in recent years. While the techniques of injecting CO2 into geological formations are well advanced, the assurance of the safety of the storage sites for many years to come remains an unanswered question. The storage of CO2 presents potential changes in the physical, chemical, and mechanical state of the geological formations and in situ reservoir brine [17,18,19,20,21][16][17][18][19][20]. An example was demonstrated in the study by Oye et al. [22][21], who showed the occurrence of microseismic events in clusters within a limited spatial area, which was attributed to CO2 injection. Hydrogen gas requires higher storage volumes due to its low volume to burn ratio [23][22]. One of the proposed storage solutions for hydrogen for future use is its storage in geological formations [24,25][23][24]. The concern of this storage mechanism is the possible induction of microseismic events due to pressure build-up as well as the loss of hydrogen in the geological formation [26][25]. Similar concerns have been attributed to the underground storage of gas [27][26].
At the end of the life cycle of a well, a well abandonment and decommissioning operation is implemented to isolate and prevent the further inflow of hydrocarbons or the migration of hydrocarbons upward, which could contaminate the upper layer water-bearing zones. However, while the techniques implored in well plugging and abandonment are well advanced, the longevity of the integrity of the well is difficult to predict. Hence in most cases, there is a need for the continuous monitoring of wellbore integrity and other previously induced microseismic events [28,29][27][28].

3. DAS in Reservoir Characterization

For a long time, three-dimensional vertical seismic profiling (3D-VSP) has been considered as appealing for imaging complex subsurface structures, both in exploration and time-lapse monitoring for the characterization of reservoirs. However, the associated costs and complexity of installing geophone arrays in a well as well as the scarcity of available wells have hampered the widespread deployment of 3D-VSP [30][29]. These challenges can essentially be reduced by the use of the novel distributed acoustic sensing (DAS) technology.
DAS uses an ordinary or engineered fiber optic cable for seismic monitoring. In its deployment, an interrogation unit (IU) is attached at the end of the fiber optic cable near or on the surface. The IU measures the deformations (contractions or extensions) along the fiber optic cable caused by propagating seismic waves. This sort of measurement is known as distributed acoustic sensing. “Distributed” because any part of the fiber cable can be deformed and logged for seismic information.
DAS measurements are straightforward in concept. A laser pulse is sent down the fiber cable by the IU. As the pulse propagates through the cable, portions of it undergo Rayleigh back-scattering due to the minute heterogeneities in the cable. When a seismic wave interacts with the cable, deforming it, it causes changes in the patterns of the back-scattered light, which is then converted into seismic data. The time it takes the back-scattered pulse to travel back to the IU allows for an accurate location of the point of deformation. Due to the fast speed of light, the entire length of the fiber optic cable can be interrogated with laser pulses at frequencies far greater than those of seismic waves. Depending on the length of the borehole, the interrogation frequencies typically range from 10 to 100 kHz, with higher frequencies known to produce a higher signal-to-noise ratio (SNR) due to redundancy. Nonetheless, the length of the borehole restricts the highest permissible frequency.
The first demonstration of the capability of use of DAS for VSP acquisition was by Mestayer et al. [31][30]. There has since been tremendous progress in the development and testing of DAS technology that has resulted in its almost unrivalled acceptance for a wide range of field seismic measurements. In relation to reservoir characterization, DAS has been applied to microseismic monitoring and analysis [32[31][32][33],33,34], hydraulic fracture monitoring [35,36][34][35] as well as in flow and production monitoring [37,38,39][36][37][38].

4. Deep Learning

Deep learning [40][39] is a branch of machine learning that has gained traction in the field of seismic data processing, analysis, and interpretation due to its computational efficiency, adaptability, and inherent ability to extract high-level features from recorded seismic waveforms with little to no manual engineering. Developed for pattern recognition in computer vision, deep learning models have high-level feature extraction mechanisms that enable them to transform raw data into a subset of feature vectors, allowing learning to take place. This makes them a perfect candidate for classification or regression tasks. The detection of seismic events is a classic example of a classification task, while inversion to locate the origin of the seismic energy can be considered as a multidimensional regression problem. The most popular deep learning architectures in seismology are recurrent neural networks (RNNs) and convolutional neural networks (CNNs). The latter is preferred for its processing speed and ability to handle large volumes of data; the former’s ability to recognize sequential patterns in the data and use those patterns to predict the next possible scenario makes it the de facto time series analysis tool.
Because deep learning models are data-driven, they require a significant amount of data for training and validation. As a result, they are best suited to processing seismic data recorded by the DAS, which collects massive amounts of data. Binder and Tura [41][40] employed convolutional neural networks to automatically detect microseismic events in the data acquired by DAS along a borehole during a hydraulic fracture operation. They compared the results with those from a surface geophone array and observed that, despite the low SNR in the DAS data, the neural network was able to detect 167 new events that were not registered by the geophones. Huot et al. [42][41] reported a 98.6% accuracy of deep learning models trained with hyperparameters obtained by Bayesian optimization on 7000 manually selected microseismic DAS events. They concluded that by the application of AI, the model was able to predict more than 100,000 events, which enhanced the prediction of the spatio-temporal fracture developments, which otherwise could not have been detected by traditional methods. Furthermore, to overcome the problem of SNR that makes the data processing challenging, Qu et al. [43][42] introduced a new methodology based on fixed segmentation coupled with a support vector machine (SVM) model. The proposed methodology allowed for the identification of the best features and the optimal number of features required for producing accurate results. From the comparative analysis, the presented model had accurate results compared to the CNN and the short-term average and long-term average ratio (STA/LTA) conventional approach. Other applications of deep learning for the detection of seismic/microseismic activities are well-documented in [44,45,46,47][43][44][45][46].
Deep learning has also been applied to tasks other than the detection and classification of seismic activities. Wamriew et al. [48][47] demonstrated the potential application of deep learning to the inversion of microseismic data. They showed that a CNN model was capable of locating microseismic events and reconstructing the velocity model simultaneously in real-time from seismic waveforms. Tanaka et al. [49][48] employed a deep learning model to perform moment tensor inversion of acoustic emissions during a hydraulic fracturing experiment of granite rock and obtained 54,727 solutions.
Due to their computational efficiency, the models can be used in the field to process the data in real-time during its acquisition, thereby scaling down the amount of data to be stored while providing necessary information that could help optimize the field operations. Huot and Biondi [50][49], Wamriew et al. [51][50], and Huot et al. [52][51] emphasized that without the complete automation of microseismic data processing, large volumes of collected data could be wasted due to human processing limitations.
It is well-established in the literature that the active and real-time recording and processing of microseismic activities is very essential for the characterization of geological formations. Right from the exploration of the field to the appraisal, the development, production, enhanced, and improved oil recovery methods, abandonment well monitoring or utilization for the storage of CO2 or H2. In addition, the challenges of the physical processing of huge volumes of microseismic data and the limitations imposed could be overcome by the implementation of automated artificial intelligence models, as have been developed in recent times that could predict events and analyze the geological changes in reservoirs.

References

  1. IEA. Electricity Market Report, July 2021; Technical Report; IEA: Paris, France, 2021.
  2. Ozarslan, A. Large-scale hydrogen energy storage in salt caverns. Int. J. Hydrogen Energy 2012, 37, 14265–14277.
  3. Simon, J.; Ferriz, A.; Correas, L. HyUnder—Hydrogen Underground Storage at Large Scale: Case Study Spain. Energy Procedia 2015, 73, 136–144.
  4. Osman, A.I.; Mehta, N.; Elgarahy, A.M.; Hefny, M.; Al-Hinai, A.; Al-Muhtaseb, A.H.; Rooney, D.W. Hydrogen production, storage, utilisation and environmental impacts: A review. Environ. Chem. Lett. 2021, 20, 153–188.
  5. Huang, X.; Meister, L.; Workman, R. Reservoir Characterization by Integration of Time-lapse Seismic and Production Data. In Proceedings of the SPE Annual Technical Conference and Exhibition, San Antonio, TX, USA, 5–8 October 1997; SPE: Houston, TX, USA, 1997.
  6. Ullo, J. Recent developments in seismic exploration and reservoir characterization. In Proceedings of the 1997 IEEE Ultrasonics Symposium Proceedings. An International Symposium (Cat. No.97CH36118), Toronto, ON, Canada, 5–8 October 1997.
  7. Jia, A.; Cheng, L. The technique of digital detailed reservoir characterization. Pet. Explor. Dev. 2010, 37, 709–715.
  8. Eidsvik, J.; Avseth, P.; Omre, H.; Mukerji, T.; Mavko, G. Stochastic reservoir characterization using prestack seismic data. Geophysics 2004, 69, 978–993.
  9. Aminzadeh, F. Reservoir Characterization: Fundamental and Applications—An Overview; Wiley: Hoboken, NJ, USA, 2021.
  10. García, S.R.M.; Hernández, J.C.; López, J.L.O. Seismic Characterisation using Artificial Intelligence Algorithms for Rock Property Distribution in Static Modeling. In Proceedings of the SPE Reservoir Characterisation and Simulation Conference and Exhibition, Abu Dhabi, United Arab Emirates, 17–19 September 2019; SPE: Houston, TX, USA, 2019.
  11. Raheem, M.W.; Shuker, A.K. Prediction by reservoir porosity using micro-seismic attribute analysis by machine learning algorithms in an Iraqi Oil Field. Turk. J. Comput. Math. Educ. (TURCOMAT) 2021, 12, 3324–3332.
  12. Afra, S.; Tarrahi, M. An Efficient EOR Screening Approach with Statistical Pattern Recognition: Impact of Rock/Fluid Feature Selection and Extraction. In Proceedings of the Offshore Technology Conference, Houston, TX, USA, 2–5 May 2016.
  13. Foulger, G.R.; Wilson, M.P.; Gluyas, J.G.; Julian, B.R.; Davies, R.J. Global review of human-induced earthquakes. Earth-Sci. Rev. 2018, 178, 438–514.
  14. Miller, A.D.; Julian, B.R.; Foulger, G.R. Three-dimensional seismic structure and moment tensors of non-double-couple earthquakes at the Hengill-Grensdalur volcanic complex, Iceland. Geophys. J. Int. 1998, 133, 309–325.
  15. Keiding, M.; Árnadóttir, T.; Jónsson, S.; Decriem, J.; Hooper, A. Plate boundary deformation and man-made subsidence around geothermal fields on the Reykjanes Peninsula, Iceland. J. Volcanol. Geotherm. Res. 2010, 194, 139–149.
  16. Pearce, J.K.; Raza, S.S.; Baublys, K.A.; Hayes, P.J.; Firouzi, M.; Rudolph, V. Unconventional CO2 Storage. In Proceedings of the 2021 Asia Pacific Unconventional Resources Technology Conference, Virtual, 16–18 November 2021.
  17. Williams-Stroud, S.; Bauer, R.; Leetaru, H.; Oye, V.; Stanek, F.; Greenberg, S.; Langet, N. Analysis of Microseismicity and Reactivated Fault Size to Assess the Potential for Felt Events by CO2 Injection in the Illinois Basin. Bull. Seismol. Soc. Am. 2020, 110, 2188–2204.
  18. Kovacs, T.; Daniel, F.P.; de Dios, C. Strategies for Injection of CO2 into Carbonate Rocks at Hontomin: Final Technical Report; Global CCS Institute, June 2015; Available online: https://www.globalccsinstitute.com/archive/hub/publications/193428/strategies-injection-co2-carbonate-rocks-hontomin-final-technical-report.pdf (accessed on 5 May 2022).
  19. Simmenes, T.; Hansen, O.R.; Eiken, O.; Teige, G.M.G.; Hermanrud, C.; Johansen, S.; Bolaas, H.M.N.; Hansen, H. Importance of Pressure Management in CO2 Storage. In Proceedings of the Offshore Technology Conference, Houston, TX, USA, 6–9 May 2013.
  20. Zoback, M.D.; Gorelick, S.M. To prevent earthquake triggering, pressure changes due to CO2 sub-injection need to be limited. Proc. Natl. Acad. Sci. USA 2015, 112, E4510.
  21. Oye, V.; Aker, E.; Daley, T.M.; Kühn, D.; Bohloli, B.; Korneev, V. Microseismic Monitoring and Interpretation of Injection Data from the in Salah CO2 Storage Site (Krechba), Algeria. Energy Procedia 2013, 37, 4191–4198.
  22. Abe, J.; Popoola, A.; Ajenifuja, E.; Popoola, O. Hydrogen energy, economy and storage: Review and recommendation. Int. J. Hydrogen Energy 2019, 44, 15072–15086.
  23. Tarkowski, R. Underground hydrogen storage: Characteristics and prospects. Renew. Sustain. Energy Rev. 2019, 105, 86–94.
  24. Lemieux, A.; Shkarupin, A.; Sharp, K. Geologic feasibility of underground hydrogen storage in Canada. Int. J. Hydrogen Energy 2020, 45, 32243–32259.
  25. Heinemann, N.; Wilkinson, M.; Haszeldine, R.S.; Fallick, A.E.; Pickup, G.E. CO2 sequestration in a UK North Sea analogue for geological carbon storage. Geology 2013, 41, 411–414.
  26. Simpson, D.W.; Leith, W. The 1976 and 1984 Gazli, USSR, earthquakes—Were they induced? Bull. Seismol. Soc. Am. 1985, 75, 1465–1468.
  27. Maxwell, S.C.; Urbancic, T.I. The role of passive microseismic monitoring in the instrumented oil field. Lead. Edge 2001, 20, 636–639.
  28. Ajayi, B.; Walker, K.; Sink, J.; Wutherich, K.; Downie, R. Using Microseismic Monitoring as a Real Time Completions Diagnostic Tool in Unconventional Reservoirs: Field Case Studies. In Proceedings of the SPE Eastern Regional Meeting, Columbus, OH, USA, 17–19 August 2011; SPE: Houston, TX, USA, 2011.
  29. Mateeva, A.; Lopez, J.; Potters, H.; Mestayer, J.; Cox, B.; Kiyashchenko, D.; Wills, P.; Grandi, S.; Hornman, K.; Kuvshinov, B.; et al. Distributed acoustic sensing for reservoir monitoring with vertical seismic profiling. Geophys. Prospect. 2014, 62, 679–692.
  30. Mestayer, J.; Cox, B.; Wills, P.; Kiyashchenko, D.; Lopez, J.; Costello, M.; Bourne, S.; Ugueto, G.; Lupton, R.; Solano, G.; et al. Field trials of distributed acoustic sensing for geophysical monitoring. In SEG Technical Program Expanded Abstracts 2011; Society of Exploration Geophysicists: Houston, TX, USA, 2011.
  31. Walter, F.; Gräff, D.; Lindner, F.; Paitz, P.; Köpfli, M.; Chmiel, M.; Fichtner, A. Distributed acoustic sensing of microseismic sources and wave propagation in glaciated terrain. Nat. Commun. 2020, 11, 2436.
  32. Hudson, T.S.; Baird, A.F.; Kendall, J.M.; Kufner, S.K.; Brisbourne, A.M.; Smith, A.M.; Butcher, A.; Chalari, A.; Clarke, A. Distributed Acoustic Sensing (DAS) for Natural Microseismicity Studies: A Case Study From Antarctica. J. Geophys. Res. Solid Earth 2021, 126, e2020JB021493.
  33. Lellouch, A.; Luo, B.; Huot, F.; Clapp, R.G.; Given, P.; Biondi, E.; Nemeth, T.; Nihei, K.T.; Biondi, B.L. Microseismic analysis over a single horizontal distributed acoustic sensing fiber using guided waves. Geophysics 2022, 87, KS83–KS95.
  34. Liu, Y.; Wu, K.; Jin, G.; Moridis, G.; Kerr, E.; Scofield, R.; Johnson, A. Fracture-Hit Detection Using LF-DAS Signals Measured during Multifracture Propagation in Unconventional Reservoirs. SPE Reserv. Eval. Amp Eng. 2020, 24, 523–535.
  35. Ichikawa, M.; Kato, M.; Uchida, S.; Tamura, K.; Kato, A.; Ito, Y.; Groot, M. Low Frequency Das Data Study with Integrated Data Analysis for Monitoring Hydraulic Fracture. In Proceedings of the 82nd EAGE Annual Conference & Exhibition, Amsterdam, The Netherlands, 18–21 October 2021; European Association of Geoscientists & Engineers: Bunnik, The Netherlands, 2021.
  36. Van der Horst, J.; den Boer, H.; in’t Panhuis, P.; Kusters, R.; Roy, D.; Ridge, A.; Godfrey, A. Fibre Optic Sensing for Improved Wellbore Surveillance. In Proceedings of the International Petroleum Technology Conference, Beijing, China, 26–28 March 2013.
  37. Finfer, D.C.; Mahue, V.; Shatalin, S.V.; Parker, T.R.; Farhadiroushan, M. Borehole Flow Monitoring using a Non-intrusive Passive Distributed Acoustic Sensing (DAS). In Proceedings of the SPE Annual Technical Conference and Exhibition, Amsterdam, The Netherlands, 27–29 October 2014; SPE: Houston, TX, USA, 2014.
  38. Naldrett, G.; Cerrahoglu, C.; Mahue, V. Production Monitoring Using Next-Generation Distributed Sensing Systems. Petrophys.—SPWLA J. Form. Eval. Reserv. Descr. 2018, 59, 496–510.
  39. LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444.
  40. Binder, G.; Tura, A. Convolutional neural networks for automated microseismic detection in downhole distributed acoustic sensing data and comparison to a surface geophone array. Geophys. Prospect. 2020, 68, 2770–2782.
  41. Huot, F.; Lellouch, A.; Given, P.; Luo, B.; Clapp, R.G.; Nemeth, T.; Nihei, K.T.; Biondi, B.L. Detection and characterization of microseismic events from fiber-optic DAS data using deep learning. arXiv 2022, arXiv:2203.07217.
  42. Qu, S.; Guan, Z.; Verschuur, E.; Chen, Y. Automatic high-resolution microseismic event detection via supervised machine learning. Geophys. J. Int. 2020, 222, 1881–1895.
  43. Hernandez, P.D.; Ramirez, J.A.; Soto, M.A. Deep-Learning-Based Earthquake Detection for Fiber-Optic Distributed Acoustic Sensing. J. Lightwave Technol. 2022, 40, 2639–2650.
  44. Shaheen, A.; bin Waheed, U.; Fehler, M.; Sokol, L.; Hanafy, S. GroningenNet: Deep Learning for Low-Magnitude Earthquake Detection on a Multi-Level Sensor Network. Sensors 2021, 21, 8080.
  45. Mousavi, S.M.; Ellsworth, W.L.; Zhu, W.; Chuang, L.Y.; Beroza, G.C. Earthquake transformer—An attentive deep-learning model for simultaneous earthquake detection and phase picking. Nat. Commun. 2020, 11, 3952.
  46. Kuyuk, H.S.; Susumu, O. Real-Time Classification of Earthquake using Deep Learning. Procedia Comput. Sci. 2018, 140, 298–305.
  47. Wamriew, D.; Charara, M.; Pissarenko, D. Joint event location and velocity model update in real-time for downhole microseismic monitoring: A deep learning approach. Comput. Geosci. 2022, 158, 104965.
  48. Tanaka, R.; Naoi, M.; Chen, Y.; Yamamoto, K.; Imakita, K.; Tsutsumi, N.; Shimoda, A.; Hiramatsu, D.; Kawakata, H.; Ishida, T.; et al. Preparatory acoustic emission activity of hydraulic fracture in granite with various viscous fluids revealed by deep learning technique. Geophys. J. Int. 2021, 226, 493–510.
  49. Huot, F.; Biondi, B. Machine learning algorithms for automated seismic ambient noise processing applied to DAS acquisition. In SEG Technical Program Expanded Abstracts 2018; Society of Exploration Geophysicists: Houston, TX, USA, 2018.
  50. Wamriew, D.; Pevzner, R.; Maltsev, E.; Pissarenko, D. Deep Neural Networks for Detection and Location of Microseismic Events and Velocity Model Inversion from Microseismic Data Acquired by Distributed Acoustic Sensing Array. Sensors 2021, 21, 6627.
  51. Huot, F.; Biondi, B.L.; Clapp, R.G. Detecting local earthquakes via fiber-optic cables in telecommunication conduits under Stanford University campus using deep learning. arXiv 2022, arXiv:2203.05932.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
Video Production Service
Feedback