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The quality of onshore seismic data is strongly affected by the characteristics of the near surface. To compensate for the distortion of travel times of seismic energy, workflows have been formulated to analyse, model, and invert surface waves. This approach requires human-intensive picking of high energy modes on conditioned semblances that represent each analysis location on the dispersion survey. In this work, we designed a globally trained supervised machine learning model to perform pixelwise binary segmentation on semblances using fully convolutional architecture with residual units to pick the fundamental mode – the mode with the highest surface-wave energy. We validated our approach on all the analysis locations of the Cooper Basin in South Australia and compared the results with current conventional methods used in the industry. Presentation Date: Wednesday, October 14, 2020 Session Start Time: 1:50 PM Presentation Time: 3:05 PM Location: 351F Presentation Type: Oral
ABSTRACT It is commonly recognized that seismicity can be induced by human activities, specifically through mining, underground construction, geothermal system development, and hydraulic fracturing. While the scope and setting of projects vary, the common consequential components of these activities is disturbing in-situ stresses within the affected zones. The disturbances may result in violent rock failures that can occur in shear, compression, and combination of those in a variety of intensity levels. While induced seismicity can be destructive to the surface, its effects are further highlighted underground, particularly due to human proximity to excavations, which raises significant safety concerns. This paper offers modeling methodologies for developing induced seismicity studies and provides examples of different settings where the trigger of seismicity may become different but the mechanism stays similar. We consider the seismicity cases induced by mining, tunneling, injection pressure, and rock temperature drop. The methodology employs energy balance calculations and estimates the energy levels available and consumed during compressive failures and fault activations. We illustrate the significance of in-situ stresses, rock brittleness, and fault characteristics in inducing seismicity during investigating roles of operational parameters, such as opening extents in excavation activities, reservoir temperatures in geothermal systems, and injection pressure in injection sites. 1. INTRODUCTION It is frequently reported that seismicity can be induced by human activities, specifically through mining, underground construction, geothermal system development, and wastewater injection. Seismicity generated by violent failures of rock in tunneling, shaft sinking, and underground mining operations represent a significant challenge for their safety and performance due to the human proximity to the excavations. Violent failures can occur in shear, compression, or in a combination mode. Compressive failures mostly occur due to high stresses exceeding the rock strength or due to a drop of rock confinement. Slip (cumulative shear displacement discontinuity) on preexisting discontinuities may also occur due to either increasing the shear stress or decreasing the normal stress acting on the discontinuity.
The Australian continent is generally in a state of compressive stress. Australian earthquakes, for which focal mechanisms have been calculated, are generally reverse-faulting events, consistent with a predominantly compressive tectonic regime. Earthquakes of moderate intensity have been reported in Queensland since the first decades of European settlement; the first reported earthquake occurring in Cape York in 1866. The Central Burnett region, just to the north of the Surat Basin (and on different terrain), remains one of the most active regions of the State, the most recent notable earthquake being in 2015 (Eidsvold, M=5.2 main-shock). Aftershocks of this event continued to be recorded some four years subsequent. Since 1937, a growing number of entities have operated seismic networks within the State, for varying purposes and with equipment of varying instrumental design and capabilities. Campaigns of seismograph installations in the late 1970s and early 1980s improved the detection threshold down to M=3; and as low as M=2.5 in parts of south-eastern Queensland (Cuthbertson and Jaume, 1996). Whilst additional seismograph networks have been periodically installed and operated since that time, the detectability threshold for very small magnitude earthquakes has remained approximately constant. Large amounts of previously uninterpreted data has been used in this study. The study considers the location of sensors and examines the Gutenberg-Richter frequency magnitude relation for Queensland and for the Surat Basin since 1986. It also discusses detection and resolution limits and provides a baseline understanding of natural seismicity and likely rates thereof.
Zhou, Xu (Louisiana State Unviersity) | Tyagi, Mayank (Louisiana State Unviersity) | Zhang, Guoyin (China University of Petroleum - Beijing) | Yu, Hao (Southwest Petroleum University) | Chen, Yangkang (Zhejiang University)
Abstract With recent developments in data acquisition and storage techniques, there exists huge amount of available data for data-driven decision making in the Oil & Gas industry. This study explores an application of using Big Data Analytics to establish the statistical relationships between seismic attribute values from a 3D seismic survey and petrophysical properties from well logs. Such relationships and models can be further used for the optimization of exploration and production operations. 3D seismic data can be used to extract various seismic attribute values at all locations within the seismic survey. Well logs provide accurate constraints on the petrophysical values along the wellbore. Big Data Analytics methods are utilized to establish the statistical relationships between seismic attributes and petrophysical data. Since seismic data are at the reservoir scale and are available at every sample cell of the seismic survey, these relationships can be used to estimate the petrophysical properties at all locations inside the seismic survey. In this study, the Teapot dome 3D seismic survey is selected to extract seismic attribute values. A set of instantaneous seismic attributes, including curvature, instantaneous phase, and trace envelope, are extracted from the 3D seismic volume. Deep Learning Neural Network models are created to establish the relationships between the input seismic attribute values from the seismic survey and petrophysical properties from well logs. Results show that a Deep Learning Neural Network model with multi-hidden layers is capable of predicting porosity values using extracted seismic attribute values from 3D seismic volumes. Ultilization of a subset of seismic attributes improves the model performance in predicting porosity values from seismic data.
ABSTRACT Near-realtime seismic monitoring of fluid injection allowed management of induced seismicity during a soft stimulation of a 6.1-km-deep geothermal well near Helsinki, Finland. A total of 18,160 m3 of fresh water were pumped into crystalline rocks over 49 days in June-July 2018. Well-head stimulation pressures were varied between 60â€“90Â MPa and flow rates between 400â€“800Â l/min. Seismic monitoring was performed with a 24-station borehole seismometer network, including a 2.2â€“2.6Â km deep 12-level array of 3 component sensors. Using near-realtime acquisition and analysis of induced-earthquake rates, locations, magnitudes, and evolution of radiated seismic versus hydraulic energy, pumping was either stopped or varied. This procedure avoided the nucleation of a regulator mandated maximum magnitude M 2.1 induced earthquake. These results are consistent with a crack-stability physics approach to controlling stimulation induced seismicity in geothermal projects. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 1:50 PM Presentation Time: 2:40 PM Location: 302B Presentation Type: Oral
ABSTRACT Enhanced 3D interpolation presented in this case study is a method of guiding interpolation of 2D seismic data along 3D geological model, generated from 2D structural models and dip fields. This allows distances of kilometers to be interpolated, something that would otherwise not be possible with conventional interpolation techniques. Two case studies will be presented, one in the Cooper Basin (onshore), located across the state border of South Australia and Queensland, another in the Beagle Bedout offshore area in Northwest Australia. Presentation Date: Monday, September 16, 2019 Session Start Time: 1:50 PM Presentation Start Time: 3:05 PM Location: Poster Station 3 Presentation Type: Poster
ABSTRACT We present a simulation of the electromagnetic (EM) response and its joint interpretation with fracking monitoring simulation during hydraulic fracturing in an unconventional reservoir. A multiphysics workflow is presented, using a criterion based on a breakdown pressure to generate and propagate the hydraulic fracturing, where both pressure response and EM response were jointly constructed. The approximate solution of Maxwell equations was obtained using a mixed Finite Element Method (FEM) combined with Leapfrog time stepping procedure. The spatial discretization of Maxwell equations is achieved using the mixed finite-element spaces of Nedelec where the electrical field have continuous tangential component across the edges of the computational mesh. Leapfrog time stepping was used for the time discretization, so that the fully discrete sequence is first solving for the electric field using Preconditioned Conjugate Gradient Iteration (PCGI) and then solving for the magnetic field. A stability analysis of this FEM method was performed. The results show that the EM response might be sensitive enough to be monitored and the magnetic field correlates better with the saturation distribution than the electric field. Furthermore, due to its relation to water saturation and effective connectivity, it is shown that EM monitoring yields additional information to determine the Stimulated Reservoir Volume (SRV). An error analysis of estimations from EM and the actual saturation size indicates that the petrophysical assumption is perhaps the most sensitive part of this study. Presentation Date: Tuesday, September 17, 2019 Session Start Time: 8:30 AM Presentation Time: 9:20 AM Location: 225C Presentation Type: Oral
Abstract Unconventional gas exploration in the Cooper Basin, Australia, has historically concentrated on fracture stimulation of tight gas sandstones within mapped structural closures. In drilling these sandstones, and other clastic reservoir targets, it has been recognised for many years that the Permian coal measures of the Toolachee, Epsilon and Patchawarra Formations record high levels of gas, often in excess of 4000 units, encountered at depths between 2500 and 3500m. Unlike shallower Coal-Seam-Gas reservoirs, which rely on de-pressuristion through de-watering to liberate adsorbed gas from the kerogen surface, deep coals are a "dry" system in which the free gas component is produced via kerogen and fracture permeability. However maintaining a consistent and commercial flow rate from deep coals alone remained enigmatic until the first dedicated fracture stimulation program of deep Permian coals was commenced in the Moomba Field in 2007. Understandings of Permian source-rock reservoirs, the roles of the coal type and rank on sorption capacity and porosity, the influence of effective pressure and depth on coal permeability and the interrelation of coal fracture permeability with in-situ stress and mechanical stratigraphy has now advanced. The deep Permian coal fairway in the Patchawarra and Nappamerri Trough of the Cooper Basin has been defined and mapped using a generative potential approach within a comprehensive 3D basin model. Net coal thicknesses from log electro-facies for 879 wells has been combined with available well maturity, TOC, HI and kerogen kinetic data, and calibrated against corrected temperatures in a basin-wide Trinity retention model which incorporates 14 mapped regional horizons. Play fairways have been overlain with observations of in-situ stress direction and fracture orientations from 3D seismic curvature volumes, FMI data and stress states from Mechanical Earth Models (MEM). Within the basin, this approach has defined a P50 in-place resource of 14.6 TCF of gas and a P10 of 20.7 TCF of gas within the deep coals of the Permian Toolachee, Epsilon and Patchawarra Formations in Senex permits, of which 8-11 TCF is within the North Patchawarra Trough. MEM's have also demonstrated that deep coal seams are consistently in a normal stress state and therefore provide excellent scope for both propagating and constraining vertical fracture growth. Work is now underway to define further those areas, within the mapped resource parameters, which provide the best opportunity to site pilot lateral wells for multi-stage fracture stimulation within deep coals.
Ma, Chunguang (Research Institute of Exploration & Development, PetroChina Dagang Oilfield Company) | Zhao, Xianzheng (School of Resources and Environment, University of Electronic Science and Technology of China) | Zhao, Qing (Research Institute of Exploration & Development, PetroChina Dagang Oilfield Company)
At present, multi-stage hydrofracturing technology aims to achieve full flow of oil and gas in the reservoir and is one of the key technologies for shale gas exploration and development. However, high uncertainty still exists in the currently available monitoring techniques for the depiction of fracturing zones. In this paper, we examine the plausibility of effectively monitoring the proppant distribution by borehole radar (BHR) surveys during the hydrofracturing stage increment through numerical simulation. The numerical simulation results demonstrate that it is possible to characterize the effective propped volume (EPV) of hydrofractured stages based on the permittivity and conductive changes in the shale gas field before and after fracturing.
Presentation Date: Tuesday, October 16, 2018
Start Time: 1:50:00 PM
Location: 212A (Anaheim Convention Center)
Presentation Type: Oral
We use least-squares migration to emphasize edge diffractions. The inverted forward modeling operator is the chain of three operators: Kirchoff modeling, azimuthal plane-wave destruction and path-summation integral filter. Azimuthal planewave destruction removes reflected energy without damaging edge diffraction signatures. Path-summation integral guides the inversion towards probable diffraction locations. We combine sparsity constraints and anisotropic smoothing in the form of shaping regularization to highlight edge diffractions. Anisotropic smoothing enforces continuity along edges. Sparsity constraints emphasize diffractions perpendicular to edges and has a denoising effect. Synthetic and field data examples illustrate the effectiveness of the proposed approach in denoisingand highlighting edge diffractions, such as channel edges and faults.
Presentation Date: Wednesday, October 17, 2018
Start Time: 8:30:00 AM
Location: 207A (Anaheim Convention Center)
Presentation Type: Oral