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Results
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...)
We present a new alternative for the joint inversion of well logs to predict the volumetric and zone parameters in hydrocarbon reservoirs. Porosity, water saturation, shale content, kerogen and matrix volumes are simultaneously estimated with the tool response function constants with a hyperparameter estimation assisted inversion of the total and spectral natural gamma-ray intensity, neutron porosity and resistivity logs. We treat the zone parameters, i.e., the physical properties of rock matrix constituents, shale, kerogen, and pore-fluids, as well as some textural parameters, as hyperparameters and estimate them in a meta-heuristic inversion procedure for the entire processing interval. The selection of inversion unknowns is based on parameter sensitivity tests, which show the automated estimation of several zone parameters is favorable and their possible range can also be specified in advance. In the outer loop of the inversion procedure, we use a real-coded genetic algorithm for the prediction of zone parameters, while we update the volumetric parameters in the inner loop in addition to the fixed values of zone parameters estimated in the previous step. We apply a linearized inversion process in the inner loop, which allows for the quick prediction of volumetric parameters along with their estimation errors from point to point along a borehole. Derived parameters such as hydrocarbon saturation and total organic content show good agreement with core laboratory data. The significance of the inversion method is in that zone parameters are extracted directly from wireline logs, which both improves the solution of the forward problem and reduces the cost of core sampling and laboratory measurements. In a field study, we demonstrate the feasibility of the inversion method using real well logs collected from a Miocene tight gas formation situated in the Derecske Trough, Pannonian Basin, East Hungary.
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.71)
- Europe > Slovakia > Pannonian Basin (0.99)
- Europe > Serbia > Pannonian Basin (0.99)
- Europe > Romania > Pannonian Basin (0.99)
- (9 more...)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
Sensitivity analysis of S-waves and their velocity measurement in slow formations from monopole acoustic logging-while-drilling
Ji, Yunjia (University of Electronic Science and Technology of China, Guilin University of Electronic Technology, Chinese Academy of Sciences) | Wang, Hua (University of Electronic Science and Technology of China, University of Electronic Science and Technology of China)
Monopole acoustic logging-while-drilling (LWD) enables the direct measurement of shear (S) wave velocity in slow formations, which has been corroborated by recent theoretical and experimental studies. However, this measurement is hampered by the weakness of the S-wave signal and the lack of techniques to amplify it. To address this challenge, we have analytically computed the monopole LWD wavefields, considering both centralized and off-center tools in various slow formations. Modeling analysis reveals that four parameters primarily influence the excitation of the formation S-wave: the formation S-wave velocity, the source-to-receiver distance, the radial distance from receiver to wellbore, and source frequency. S-wave signals can be enhanced by judiciously optimizing these parameters during tool design. Furthermore, our research suggests that the S-wave velocity can be accurately extracted through the slowness-time correlation method only when formation S-wave velocities are in a suitable range. This is because an overly high S-wave velocity causes shear arrivals to be interfered with the inner Stoneley mode, whereas an ultra-slow formation S-wave velocity results in S-wave signals too faint to detect. For the LWD model with an off-center tool, simulations demonstrate that tool eccentricity, especially large eccentricity, can amplify the shear wave and improve its measurement accuracy, provided that waveforms received in the direction of tool movement are used. In a very slow formation, we successfully extracted the S-wave velocity from synthetic full-wave data at that azimuth under conditions of large eccentricity, a task not achievable with a centralized instrument.
- Research Report > New Finding (1.00)
- Research Report > Experimental Study (0.87)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Well Drilling > Drilling Measurement, Data Acquisition and Automation > Logging while drilling (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
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)
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...)
LITHOCODIUM MOUND IDENTIFICATION USING LWD IMAGE LOG AND QUANTIFIED CUTTING ANALYSIS VALIDATION WITH ANALOGUES
Perrin, Christian (North Oil Company) | Pointer, Chay (North Oil Company) | Al-Mohannadi, Ghada (North Oil Company) | Sen, Shantanu (North Oil Company) | Buraimoh, Muse Ajadi (QatarEnergy)
Lithocodium mounds are early Cretaceous sedimentary structures described in the literature from outcrops, however, never described in the subsurface. The objective of this work is to identify and characterize Lithocodium mounds in the subsurface along a 25,000ft horizontal well. Drill cuttings sampled at a 100ft interval are observed in thin sections to define and quantify key sedimentary indicators (bioclasts, facies, and texture). Logging-while-drilling (LWD) GR, density, neutron, and resistivity logs are acquired along with the LWD high-resolution borehole image (BHI) log. Bedding dips from BHI data, interpreted along the horizontal well, enabled the reconstruction of the reservoir paleotopography. In particular, the alternation of dip azimuth combined with the facies interpretation from the thin sections supported the interpretation of eight distinct mound structures. An assessment of their overall geometry confirmed the mound shape to be subcircular, consistent with the subcircular geometries observed in Oman at the outcrop. The inferred dimensions of the mounds are comparable with the Aptian Lithocodium mounds in Oman (3040m), and their intermound organization resembles that of the Albian mounds in Texas. This work demonstrates the value of analyzing cuttings to complement image log interpretation and the value of outcrop analogs for interpreting sedimentary structures. For the first time, the subsurface identification and characterization of Lithocodium mounds and intermounds are achieved.
- North America > United States > Texas (0.48)
- Asia > Middle East > Oman (0.45)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (1.00)
- Geology > Sedimentary Geology > Depositional Environment (0.93)
- Geology > Geological Subdiscipline > Stratigraphy (0.66)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (0.48)
- Well Drilling > Drilling Operations (1.00)
- Well Drilling > Drilling Measurement, Data Acquisition and Automation > Logging while drilling (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
ABSTRACT Recent induced polarization studies suggest that the real part of surface conductivity () scales linearly with the imaginary conductivity ( = ) or normalized chargeability (Mn) for a range of soil types. The coefficients of this relationship l and l_Mn (l = / or l_Mn = Mn/) allow the separation of the surface and electrolytic conductivities from the bulk conductivity. However, the dependence of these constants on varying soil physicochemical properties, including under unsaturated conditions, is yet to be assessed. Using estimates of from 18 undisturbed soil samples from a restored wetland and measured over a frequency range of 0.01 Hz to 10 kHz, the and were compared with the laboratory measurements of soil properties. Also, l and l_Mn were calculated for each soil sample and regressed them against the soil properties. We find an apparent dependence of l on soil texture, bulk density, organic matter, and moisture contents, with coefficients of determination () ranging from 0.5 to 0.65 at low frequencies (e.g., 1 Hz) but not at high frequencies (e.g., 936 Hz). This dependence of l on soil texture results from the insensitivity of at low frequency to and, by implication, to the soil properties controlling . In contrast, l_Mn indicates no correlation with the soil properties because Mn is linearly correlated with and correlated with the soil properties controlling . Our results call for caution on the application of at a single frequency as a proxy of because is not necessarily correlated with across all soil types. Although using l_Mn derived from multifrequency measurements overcomes this limitation, field acquisition of spectral information (e.g., up to 1000 Hz) remains a challenge.
- Geophysics > Electromagnetic Surveying (0.69)
- Geophysics > Borehole Geophysics (0.49)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (0.67)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (0.49)
ABSTRACT Unlike the common situation for which vertical wells penetrate horizontal layers, the trajectory of high-angle wells is usually not aligned with the principal axes of elastic rock properties. Borehole sonic measurements acquired in high-angle wells in general do not exhibit axial symmetry in the vicinity of bed boundaries and thin layers, and sonic waveforms remain strongly affected by the corresponding contrast in elastic properties across bed boundaries. The latter conditions often demand sophisticated and time-consuming numerical modeling to reliably interpret borehole sonic measurements into rock elastic properties. The problem is circumvented by implementing the eikonal equation based on the fast marching method to (1) calculate first-arrival times of borehole acoustic waveforms and (2) trace raypaths between sonic transmitters and receivers in high-angle wells. Furthermore, first-arrival times of P and S waves are calculated at different azimuthal receivers included in wireline borehole sonic instruments and are verified against waveforms obtained via 3D finite-difference time-domain simulations. Calculations of traveltimes, wavefronts, and raypaths for challenging synthetic examples with effects due to formation anisotropy and different inclination angles indicate a transition from a head wave to a boundary-induced refracted wave as the borehole sonic instrument moves across bed boundaries. Apparent slownesses obtained from first-arrival times at receivers can be faster or slower than the actual slownesses of rock formations surrounding the borehole, depending on formation dip, azimuth, anisotropy, and bed boundaries. Differences in apparent acoustic slownesses measured by adjacent azimuthal receivers reflect the behavior of wave propagation within the borehole and across bed boundaries and can be used to estimate bed-boundary orientation and anisotropy. The high-frequency approximation of traveltimes obtained with the eikonal equation saves more than 99% of calculation time with acceptable numerical errors, with respect to rigorous time-domain numerical simulation of the wave equation, and is therefore amenable to inversion-based measurement interpretation. Apparent slownesses extracted from acoustic arrival times suggest a potential method for estimating formation elastic properties and inferring boundary geometries.
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Processing (0.93)
- Well Drilling > Well Planning > Trajectory design (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (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)
Generating 3D lithology probability volumes using poststack inversion, probabilistic neural networks, and Bayesian classification — A case study from the mixed carbonate and siliciclastic deposits of the Cisco Group of the Eastern Shelf of the Permian Basin, north-central Texas
Karakaya, Sarp (The University of Texas at Austin, The University of Texas at Austin) | Ogiesoba, Osareni C. (The University of Texas at Austin) | Olariu, Cornel (The University of Texas at Austin, Research National Institute of Marine Geology and Geo-ecology (GeoEcoMar)) | Bhattacharya, Shuvajit (The University of Texas at Austin)
ABSTRACT The deposition and mixing of carbonates and siliciclastics in the Cisco Group of the Eastern Shelf of the Permian Basin are complicated by the temporal overlap between icehouse eustatic sea-level oscillations and fluctuations in sediment influx due to the rejuvenation of the Ouachita fold belt. Previous investigators have used well-log correlation as the primary tool in their interpretations of the area’s reciprocal depositional model, but well-log correlation alone cannot explain the full range of spatial lithology variations in the system. To better understand the lithology variation in the area, we use an integrated technique that combines wireline log information from 17 wells with 625 km 3D seismic data through poststack seismic inversion, probabilistic neural networks (PNNs), and Bayesian classification. We use deterministic matrix inversion to derive lithology classes from well logs. Crossplot analyses reveal that the acoustic impedance and neutron porosity log pair can be used to differentiate lithologies. We perform model-based poststack inversion to generate a P-impedance volume and use PNNs to generate a neutron porosity volume. We combine these volumes through supervised Bayesian classification to generate lithology probability volumes for each lithology and a most probable lithology volume throughout the seismic data. The lithology volumes highlight the dominant lithologies (carbonate, shale, sand, and mixed) that allowed the interpretation of major carbonate platforms, sand-to-shale ratio variations, carbonate buildups between wells, and channel fill lithologies. Our semiautomated lithology detection workflow applies to regional studies and is also valid for reservoir-scale studies to determine variations in lithologies.
- Phanerozoic > Paleozoic > Permian (1.00)
- Phanerozoic > Paleozoic > Carboniferous > Pennsylvanian (0.48)
- Geology > Structural Geology > Tectonics > Compressional Tectonics > Fold and Thrust Belt (1.00)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.69)
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
- Geophysics > Borehole Geophysics (1.00)
- North America > United States > Wyoming > Uinta Basin (0.99)
- North America > United States > West Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- (45 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (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)
Quantifying the influence of clay-bound water on wave dispersion and attenuation signatures of shale: An experimental study
Long, Teng (University of Houston) | Qin, Xuan (University of Houston) | Wei, Qianqian (University of Houston) | Zhao, Luanxiao (Tongji University) | Wang, Yang (University of Houston) | Chen, Feng (University of Houston) | Myers, Michael T. (University of Houston) | Zheng, Yingcai (University of Houston) | Han, De-Hua (University of Houston)
ABSTRACT Understanding the elastic and attenuation signatures of shales is of considerable interest for unconventional reservoir characterization and sealing capacity evaluation for CO2 sequestration and nuclear waste disposal. We have conducted laboratory measurements on seven shale samples at seismic frequencies (2–100 Hz) to study the effects of clay-bound water (CBW) on their wave dispersion and attenuation signatures. With nuclear magnetic resonance and a helium porosimeter, the volume of CBW in the shale samples is quantified. The forced-oscillation measurement reveals that Young’s modulus exhibits a continuous dispersion trend from 2 to 100 Hz. The extensional attenuation () shows a weak frequency and pressure dependence on effective pressure ranging from 5 to 35 MPa. The magnitude of extensional attenuation shows a positive correlation with CBW, with an value of 0.89. It is found that 4% of CBW in the rock frame causes approximately a 5% modulus increase from 2 to 100 Hz. We adopt a constant model for assigning frequency-dependent bulk and shear moduli to the CBW in the rock-physics modeling, which can fit the experimental data of modulus dispersion and attenuation well, indicating that the bulk and shear moduli of CBW in shales might behave viscoelastically.
- Research Report > New Finding (0.50)
- Research Report > Experimental Study (0.40)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Mineral > Silicate (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (0.67)
- North America > United States > New Mexico > San Juan Basin > San Juan Basin Field > Mancos Formation (0.99)
- North America > United States > Colorado > San Juan Basin > San Juan Basin Field > Mancos Formation (0.99)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (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)