Well & Reservoir Surveillance and Monitoring
During geophysical exploration, inpainting defective logging images caused by mismatches between logging tools and borehole sizes can affect fracture and hole identification, petrographic analysis and stratigraphic studies. However, existing methods do not describe stratigraphic continuity enough. Also, they ignore the completeness of characterization in terms of fractures, gravel structures, and fine-grained textures in the logging images. To address these issues, we propose a deep learning method for inpainting stratigraphic features. First, to enhance the continuity of image inpainting, we build a generative adversarial network (GAN) and train it on numerous natural images to extract relevant features that guide the recovery of continuity characteristics. Second, to ensure complete structural and textural features are found in geological formations, we introduce a feature-extraction-fusion module with a co-occurrence mechanism consisting of channel attention(CA) and self-attention(SA). CA improves texture effects by adaptively adjusting control parameters based on highly correlated prior features from electrical logging images. SA captures long-range contextual associations across pre-inpainted gaps to improve completeness in fractures and gravels structure representation. The proposed method has been tested on various borehole images demonstrating its reliability and robustness.
- Geology > Geological Subdiscipline > Stratigraphy (0.74)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.46)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Borehole imaging and wellbore seismic (1.00)
- (2 more...)
In this episode, host Andrew Geary speaks with Ariel Lellouch and Tieyuan Zhu on distributed acoustic sensing (DAS), the featured special section in November's The Leading Edge. Ariel and Tieyuan highlight new developments in algorithms impacting microseismic, new findings for hydraulic fracturing, and discuss their disagreement for the current rate the geophysics industry is adopting and utilizing DAS. This is an exciting conversation on technology that has a wide range of applications for geophysics.
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Well Completion > Hydraulic Fracturing (0.77)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (0.77)
Petroleum Engineering, University of Houston, 2. Metarock Laboratories, 3. Department of Earth and Atmospheric Sciences, University of Houston) 16:00-16:30 Break and Walk to Bizzell Museum 16:30-17:30 Tour: History of Science Collections, Bizzell Memorial Library, The University of Oklahoma 17:30-19:00 Networking Reception: Thurman J. White Forum Building
- Research Report > New Finding (0.93)
- Overview (0.68)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Mineral (0.72)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.68)
- (2 more...)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.93)
Distributed fiber optic sensing is a rapidly developing technology with significant disruptive potential in multiple applications in many -- if not all -- of these activities. It allows the deployment of thousands of sensors that provide vital information about complex processes, enabling new optimization options and significant energy and cost savings. Fiber optic sensing can play a major role in monitoring energy production, distribution and CCS/CCUS. The workshop will focus on recent developments in the area, including high-value use cases and technology drivers. We aim to bring together current and prospective technology end-users and problem owners, sensor hardware researchers tools, fibers, cables, interrogators and application experts for seismic imaging, downhole monitoring, etc.
- Well Completion > Completion Monitoring Systems/Intelligent Wells > Downhole sensors & control equipment (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
- Asia > China > Shanxi > Ordos Basin > Changqing Field (0.99)
- Asia > China > Shaanxi > Ordos Basin > Changqing Field (0.99)
- Asia > China > Ningxia > Ordos Basin > Changqing Field (0.99)
- (2 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
Facies classification of image logs plays a vital role in reservoir characterization, especially in the heterogeneous and anisotropic carbonate formations of the Brazilian pre-salt region. Although manual classification remains the industry standard for handling the complexity and diversity of image logs, it has notable disadvantages of being time-consuming, labor-intensive, subjective, and non-repeatable. Recent advancements in machine learning offer promising solutions for automation and acceleration. However, previous attempts to train deep neural networks for facies identification have struggled to generalize to new data due to insufficient labeled data and the inherent intricacy of image logs. Additionally, human errors in manual labels further hinder the performance of trained models. To overcome these challenges, we propose adopting the state-of-the-art SwinV2-Unet to provide depthwise facies classification for Brazilian pre-salt acoustic image logs. The training process incorporates transfer learning to mitigate overfitting and confident learning to address label errors. Through a k-fold cross-validation experiment, with each fold spanning over 350 meters, we achieve an impressive macro F1 score of 0.90 for out-of-sample predictions. This significantly surpasses the previous model modified from the widely recognized U-Net, which provides a macro F1 score of 0.68. These findings highlight the effectiveness of the employed enhancements, including the adoption of an improved neural network and an enhanced training strategy. Moreover, our SwinV2-Unet enables highly efficient and accurate facies analysis of the complex yet informative image logs, significantly advancing our understanding of hydrocarbon reservoirs, saving human effort, and improving productivity.
- Geology > Structural Geology > Tectonics > Salt Tectonics (1.00)
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.67)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Borehole imaging and wellbore seismic (1.00)
- (2 more...)
Distributed acoustic sensing for seismic surface wave data acquisition in an intertidal environment
Trafford, Andrew (University College Dublin) | Ellwood, Robert (Optasense Limited, QinetiQ) | Godfrey, Alastair (Optasense Limited, Indeximate Limited) | Minto, Christopher (Optasense Limited, Indeximate Limited) | Donohue, Shane (University College Dublin)
This paper assesses the use of Distributed Acoustic Sensing (DAS) for shallow marine seismic investigations, in particular the collection of seismic surface wave data, in an intertidal setting. The paper considers appropriate selection and directional sensitivity of fiber optic cables and validates the measured data with respect to conventional seismic data acquisition approaches ,using geophones and hydrophones, along with independent borehole and Seismic Cone Penetration Test (SCPT) data. In terms of cable selection, a reduction of amplitude and frequency response of an armored cable is observed, when compared to an unarmored cable. For seismic surface wave surveys in an offshore environment where the cable would need to withstand significant stresses, the use of the armored variant with limited loss in frequency response may be acceptable, from a practical perspective. The DAS approach has also shown good consistency with conventional means of surface wave data acquisition, and the inverted Vs is also very consistent with downhole SCPT data. Observed differences in phase velocity between high tide (Scholte wave propagation) and low tide (Rayleigh wave propagation) are not thought to be related to the particular type of interface wave due to shallow water depth. These differences are more likely to be related to the development of capillary forces in the partially saturated granular medium at low tide. Overall, this study demonstrates that the proposed novel approach of DAS using seabed fiber-optic cables in the intertidal environment is capable of rapidly providing near-surface shear wave velocity data across considerable spatial scales (multi-km) at high resolution, beneficial for the design of subsea cables routes and landfall locations. The associated reduction in deployment and survey duration, when compared to conventional approaches, is particularly important when working in the marine environment due to potentially short weather windows and expensive downtime.
- Europe (1.00)
- North America > United States > Illinois > Madison County (0.24)
- Research Report > New Finding (0.68)
- Research Report > Experimental Study (0.54)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
Introduction to special section: Borehole image data applications in reservoir characterization ? Case studies and updates on new developments
Li, Bingjian (Blackriver Geoscience LLC) | Egorov, Vsevolod (GeoExpera) | Perona, Ricardo (Repsol) | Haddad, David (Apache Corporation) | Sementelli, Katy (Woodside Energy) | Xu, Chicheng (Aramco Americas Company) | Mardi, Chrystianto (BPX Energy)
Borehole image data have played an important role in the oil and gas industry for decades, providing invaluable insights into hydrocarbon exploration, reservoir appraisal, and development. Recent advancements in borehole image technologies, encompassing data acquisition, processing, and interpretation, have ushered in a new era of possibilities. Geoscientists have expanded the applications of image data, progressing from basic natural fracture detection to comprehensive reservoir characterization. This special section explores significant advances in sedimentological and structural interpretation, full-scale fracture and fault analysis, wellbore geomechanics, reservoir heterogeneity evaluation, and 3-D reservoir modeling. Applications of borehole image log data have transcended reservoir types, spanning clastics, carbonates, naturally fractured basements, and unconventional shales. With these developments in mind, we have invited submissions that showcase studies utilizing borehole image log data for the successful characterization of any reservoir type, along with related case studies of interest to the exploration and development community. The overwhelming response to our call-for-papers resulted in the selection of seven high-quality contributions for inclusion in this special publication. Mohebian et al. revolutionize fracture identification by employing the YOLOv5 deep learning algorithm on borehole image logs, shifting from manual to automated processes.
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.56)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Asia > Kazakhstan > West Kazakhstan > Uralsk Region > Precaspian Basin > Karachaganak Field (0.99)
- Asia > China > Bohai Basin (0.99)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Borehole imaging and wellbore seismic (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
This course covers a brief introduction to multiphase measurements. This will include their value proposition, hardware selection with placement considerations and integration of the hardware and data. It will further focus on 3-phase in-line measurements and not necessarily on measurement after partial separation. Measurement of produced assets (gas, oil and water) is essential for reservoir maintenance, production optimization, marketing, and other disciplines. When assets change hands, a robust understanding of the measurement, its uncertainty, benefits and challenges with these measurements enable the parties to make the right decisions.
- Africa (0.32)
- North America > Canada > Alberta (0.17)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Downhole and wellsite flow metering (1.00)
- Management > Professionalism, Training, and Education (1.00)
- Facilities Design, Construction and Operation > Measurement and Control > Multiphase measurement (0.79)
This course provides a comprehensive methodology for the diagnosis, analysis, and forecasting of well production data in unconventional resources. An extensive evaluation of the diagnostic tools for assessing data viability, checking data correlation along with flow regime identification is presented. The principal focus is to diagnose the characteristic flow regimes associated with well production and apply methodologies to estimate performance parameters and forecast production. These methodologies include simple analytical tools, decline curves, and more complex techniques such as nonlinear numerical simulation. Examples from tight gas sands, gas shales, and liquids-rich shale systems will illustrate the theoretical considerations and practical aspects.
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production data management (0.68)
- Management > Energy Economics > Unconventional resource economics (0.68)