Field Projects

 Fasken Oil and Ranch, Permian Basin, Texas

The Fasken Oil and Ranch dataset ranges from the Central Basin Platform into the Midland Basin with 278 square miles of 3D seismic, an inset area of 9C3D seismic, 3 9C3D VSPs, well log data, core data, and production data. With RCP Phase XXI, the Fasken team will continue detailed research into deepwater carbonate mass failures and expand our research into Upper Wolfcamp horizontal well production prediction, machine-learning-based Wolfcamp rock-physics facies classifications, utilizing DAS for hydraulic fracture and production optimization, and saltwater disposal in the San Andres Formation.

• Seismic, well log, and core data derived carbonate-facies production and stimulation behavior in proven Dean and Wolfcamp reservoirs and identification of additionally prospective zones.
• Integration of seismic, well log, and core to evaluate heterogeneity in the Wolfberry Formation and the resulting impact on horizontal well production.
• Machine Learning based rock physics facies models in the Wolfcamp to define facies classifications.
• Hydraulic fracture and production optimization utilizing DAS and microseismic data.
• Saltwater disposal dispersal in the San Andres Formation.

Fasken C-Ranch 3D seismic coverage map and type seismic section. (R. Bianco, Fasken Oil & Ranch, 2024)

Spectral decomposition of Midland Failure in the Upper Leonardian with interpreted stuctural domains. (M. Braunscheidel, 2025)

  E-FWI ENHANCED WORKFLOW FOR JOINT PP/PS INVERSION, NORTH SEA

Joint PP/PS inversion is a powerful technique for integrating compressional (PP) and converted-wave (PS) seismic data to estimate P-impedance (Ip), S-impedance (Is), and density from amplitude variation with angle (AVA) analysis, enabling quantitative characterization by extracting engineering reservoir properties. However, traditional workflows often depend on low-frequency (LF) background models interpolated from sparse well logs, which can introduce inaccuracies and fail to capture lateral heterogeneities in areas with limited well control. To address this, the research introduces a workflow that utilizes elastic full-waveform inversion (eFWI)-derived velocity models to create low-frequency impedance models. This approach is demonstrated through 4D joint PP/PS inversion on seismic data from the Edvard Grieg Field in the North Sea. By incorporating high-resolution Vp and Vs models from eFWI, the workflow enhances the reliability of inversion results, particularly in laterally variable reservoirs.

 Joint surface and DAS VSP FWI, North Sea

This study proposes a joint inversion of surface and VSP Distributed Acoustic Sensing (DAS) data within a Full Waveform Inversion (FWI) framework to improve density estimation. By combining the broad lateral coverage of surface DAS with the high vertical resolution and near-well sensitivity of VSP DAS, the joint approach reduces the inherent ambiguity in density inversion. The method will first be validated on synthetic data before being applied to Edvard Grieg Oil Field in the Norwegian North Sea, enabling more accurate and reliable subsurface reservoir characterization.

Edvard Grieg Field DAS VSP and Surface Seismic coverage. (R. Nasution, 2025)

 Surface DAS Experiment, North Sea

The Poseidon Surface DAS dataset was acquired in the southern Norwegian North Sea in September 2023 during a 3D seismic survey designed to evaluate distributed acoustic sensing (DAS) for subsurface monitoring applications such as carbon capture and storage (CCS). The experiment combines a conventional towed-streamer seismic survey with DAS recordings from a subsea power-from-shore fiber-optic cable connected to the Valhall field. The acquisition includes ~250,000 seismic shots forming a dense 3D shot carpet, while the fiber cable crossing the survey area serves as a continuous receiver array with ~1 m channel spacing. Initial research focuses on understanding the seismic response of fiber-optic cables, the influence of shooting geometry and cable orientation on DAS signals, and the potential use of existing offshore fiber infrastructure for large-scale seismic monitoring.

  Bakken Mariner, North Dakota

The Mariner project investigates a novel method that leverages low-frequency distributed acoustic sensing (LF-DAS) combined with external pressure gauge data to quantitatively evaluate cement quality and detect annular pressure communication in horizontal monitor wells. Effective stage isolation is critical for stimulation efficiency during plug-and-perf operations, and compromised cement bonding can lead to unintended behind-casing pressure pathways. Utilizing field measurements from a Bakken multi-well pad instrumented with permanent fiber-optic cables, this research demonstrates how LF-DAS strain signals can capture fluid migration along partially debonded cement-formation interfaces. By identifying poor cement quality ahead of stimulation, this integrated analysis offers a cost-effective alternative to conventional cement bond logging. Ultimately, these early insights enable proactive adjustments to completion designs, mitigating leakage effects and enhancing overall resource recovery.

  E-FWI multi-component long offset OBN SEISMIC Field studies

GOM (Offshore Texas) Ultra-wide offset 3D OBN data (maximum offsets in the inline and crossline directions are 65 km and 40 km) have been acquired in the GOM. To date, these data have been used by commercial vendors for Full Waveform Inversion (FWI) and imaging (hydrophone component only). RCP will examine the 3C geophone components. Given the structural complexity of this region of the GOM at these large offsets, the vertical component will also measure S-waves, and the horizontal component will also measure P-waves. RCP will analyze the data to understand mode mixing and how to efficiently use this new data type. We will also examine the potential utility of converted waves for reservoir characterization in mini-basins using joint PP-PS prestack inversion. This field research project utilizing extracted data modes will improve FWI application. Further, larger offsets can be used for better S-impedance and density inversion to improve reservoir characterization.

GORGON (Offshore Australia) 3D OBN data have been acquired in the NW offshore Australia over the Gorgon Field. Similar to other OBN surveys, these data have been used by commercial vendors for Full Waveform Inversion (FWI) and imaging (hydrophone component only). RCP will analyze all 3 components of the full-azimuth data to improve imaging and interpretation of the structurally complex subsurface. Research will encompass FWI approaches (acoustic and elastic) with an emphasis on using the multi-component (vertical and horizontal) and multi-mode (PP and PS) datasets.

  Denoising and converted wave imaging 4d DAS VSP, GOM

SeisDiff-denoNIA is a diffusion-based seismic denoising framework designed for challenging DAS-VSP data acquired in the field. Unlike conventional machine-learning methods that rely on synthetic clean–noisy training pairs, this approach learns directly from field noise, allowing it to adapt to complex noise sources such as tube waves, fading, coupling noise, and production-related disturbances. Tests on synthetic and field datasets (Shell Mar 4D DAS-VSP dataset) show improved noise suppression while preserving seismic reflections. The method also enhances pre-stack migration results and supports seismic monitoring during active production without requiring well shut-ins.

 Bayou Bend CCUS potential, GOM

This study presents a site-specific reservoir characterization and CO2 monitoring feasibility assessment offshore Jefferson County, Texas. Using an integrated methodology that combining rock physics modeling and prestack seismic inversion, we developed a geological model that captures subsurface heterogeneity to simulate CO2 migration behavior. A reservoir simulation injecting 2.5 Mt of CO2 annually over 15 years — followed by a 100-year migration period — was then modeled to determine the ultimate reservoir storage capability. The study also evaluated the viability of cost-effective seismic monitoring approaches, specifically walkaway Vertical Seismic Profile (VSP), using Reverse Time Migration and time-lapse Full Waveform methods. This study highlights that incorporating seismic-derived properties yields meaningfully different plume distribution outcomes than those based on well data alone, underscoring the importance of seismic integration in CO2 storage planning.

 e-FWI, Santos Basin, offshore brazil

Converted waves can improve the accuracy and robustness of elastic full-waveform inversion (E-FWI). This study combines synthetic experiments to evaluate sensitivity to shear-wave velocity (Vs) with applications to real multi-component seismic data to assess practical imaging improvements. The goal is to enhance subsurface characterization by leveraging additional information from converted waves.

 hydraulic fracture characterization in a geothermal reservoir, utah

Characterize fluid flow in faults and fractures in an enhanced geothermal setting utilizing hydro-mechanical pressure diffusion models to quantify the diffusivity of a fault between two horizontal wells in an EGS field. The low-frequency DAS (LF-DAS) strain response in one well responds to pressure changes in the other well via pore pressure diffusion through a fault zone. Using hydro-mechanical models, we can match the field data and thus quantify the diffusivity in the time domain as well as the frequency domain, using an analytical transfer function relating input and response spectra to diffusivity.

  reservoir bitumen characterization, Kuwait

This research employs an advanced reservoir characterization framework to delineate the spatial distribution and occurrence of bitumen and pyrobitumen within the study area. By synergistically integrating multi-scale datasets—comprising legacy well logs, core and cuttings samples, and geomechanical data—the study employs machine-learning-driven inversion to map this complex hydrocarbon system. In the Middle Marrat Formation, where carbonate lithofacies frequently exhibit impermeable barrier characteristics, the presence of bitumen and pyrobitumen significantly attenuates reservoir flow capacity and overall producibility. While Nuclear Magnetic Resonance (NMR) spectroscopy remains the high-reliability method for bitumen detection, this study utilizes predictive analytics to extrapolate the distribution of both components in intervals lacking NMR coverage, ensuring a robust regional characterization.