EAGE GeoTech 2024 Fourth EAGE Workshop on Practical Reservoir Monitoring

This paper explores the challenge of non-stationary in seismic signals for reservoir characterization in geophysics. Traditional seismic inversion methods, based on stationary assumptions, are re-evaluated with a novel deep learning approach for modelling time-varying wavelets. This technique aims to align more closely with the non-linear and complex nature of seismic data. The study leverages the F3 block dataset from the Netherlands, an open-source, diverse dataset ideal for examining non-stationary seismic data, for evaluation. The findings of this study subtly hint at an emerging focus for seismic inversion research, towards a deeper understanding of seismic wave propagation effects.

Autosteering in Unconventional Reservoir: A Case Study for Resteering Multi-Horizontal Wells Simultaneously at Eagle Ford Basin, utilizing Machine Learning Application.

The abstract discusses the application of autosteering in the context of unconventional reservoirs, focusing on the Eagle Ford basin in the USA. The aim is to enhance drilling efficiency and accuracy, particularly in low permeability reservoirs, through the use of horizontal wells. Autosteering is proposed as a solution to navigate precise geological structures efficiently. The paper introduces an autosteering model using Machine Learning (ML) and compares its benefits to traditional manual correlation methods.

The autosteering model proves to expedite decision-making, significantly reducing interpretation time while maintaining high accuracy. The research highlights the practical application of autosteering in unconventional reservoirs, emphasizing its potential to streamline drilling operations and enhance efficiency, setting a precedent for the future of navigation in the oil and gas industry.

The overall objective of the new RamonCO project is “to develop multi-dimensional robust conformance assessment methodology and advance risk-based monitoring and societal implementation strategies”, and thereby contribute to acceleration of the CO2 storage project permitting phase and secure containment and conformance during the project execution phase. Specific objectives include:

• Develop a framework for determining site-specific detection thresholds for field scale, multi-physics, multi-modal and multi-scale data types.
• Develop and test inversion framework for field scale inversion of multi-physics monitoring data.
• Develop and implement a methodology for including societal and environmental risk in common risk evaluation tools and assessment.
• Develop and implement a framework for risk-based value of information (VoI) analysis of CO2 storage monitoring

Equinor conducted a Distributed Acoustic Sensing (DAS) field trial by acquiring active DAS and Permanent Reservoir Monitoring (PRM) conventional accelerometer and hydrophone seismic data simultaneously along the same 9-km long cable, over the Johan Sverdrup field. The quality of both DAS and PRM inline acceleration along the X horizontal axis (AX) PP images was unexpected. Why was it a surprise? Because the power of stacking on final signal-to-noise ratio was underestimated and observation of pre-stack data might have been misleading. The amplitudes recorded on these sensors a priori wrong for PP reflection recording are very weak on individual seismic traces and invisible on pre-stack data but focus effectively to the stack. Mirror imaging improves the overall quality of DAS and PRM AX PP images, thanks to higher fold and better noise attenuation by stacking. The noises that have the greatest impact on image quality are the Scholte waves and associated back-scattered waves. Their filtering is much more effective on DAS data than on PRM AX data thanks to the very fine spatial sampling of DAS data down to 2 meters, compared to the PRM AX sampling of 50 meters.

Within the Energy Transition Sector, there is a clear drive for cost-effective and high-resolution monitoring solutions for carbon capture and storage (CCS), as well as shallow hazard analysis for windfarm planning and de-risking of prospective geothermal or hydrogen wells. In 2022, a field trial for a 3D eXtended High Resolution (XHR) survey was conducted over the Sleipner CCS area ( Dehghan-Niri et al., 2023 ) to assess the reliability of the XHR acquisition system and its suitability and commercial effectiveness for CCS monitoring. The trial involved acquisition of a 5x2.5 km area centred over the Sleipner CO2 plume, and was optimized for imaging of the shallow overburden and the CO2 plume, located between depths of 800–1000 m. The focus of this paper will be the processing of the XHR data, testing its capabilities of imaging the CO2 plume and exploring high frequency velocity model building including Time-lag Full-waveform Inversion (TLFWI) ( Wang et al., 2019 ) up to 200 Hz.

Jebal AlQamar (J. Qamar), a Permian–Triassic highly recrystallized limestone located southwest of the Dibba Zone in Northern UAE, is one of the Oman Exotic Blocks. It originated as a horst structure before the Late Triassic during the rifting of the NE Oman Passive Margin, uniquely positioned atop a Palaeozoic basement, unlike other Oman exotics with oceanic seamount substrates. This study focuses on the geological contact types in the J. Qamar area to understand its structural relationships and evolution. Data collected and integrated into a GIS database reveal J. Qamar’s structural framework and its connection with adjacent units. The geological architecture shows that the basement units (Semail Ophiolite and its metamorphic sole) and J. Qamar limestone were involved in the southwestward subduction zone during the Late Cretaceous. J. Qamar limestone was emplaced onto the Shamal Chert Fm. During exhumation, the system reached shallower depths (∼15km) in the subduction zone, transitioning to a ductile-brittle phase and forming cohesive fault rocks like cataclasite. The preserved structures differentiate between deformations caused by the Semail Ophiolite’s brittle phase (SW-directed) and the Late Oligocene Hegab Thrust (NW-directed).

The quality of high-resolution marine seismic data is strongly dependent on the knowledge of relative hydrophone and source positions in the water. Unfortunately, Ultra-High Resolution (UHR) streamers used for this purpose often lack GPS and RTK instruments, leaving the hydrophone depth and drift to be interpolated between head and tail buoys. We propose to embed a new type of fiber optic based sensor to monitor the deformation of the streamer in the water in 3D and in real-time. To this end, we discuss the methods of Fiber Optic Shape-Sensing (FOSS); we describe the novel design of a dedicated sensor for dynamic and long-range shape tracking; finally, we present the results of proof-of-concept flume experiments.