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Over the past 35 years, geothermal projects have been developed in the Upper Rhine Graben (URG) to exploit deep geothermal energy. Underneath approximately 2 km of sedimentary deposits, the deep target consists of a granitic basement, which is highly fractured and hydrothermally altered. Therefore, it has high potential as a geothermal reservoir.

Deterministic seismic inversion methods have been successfully used in many exploration and production projects in the petroleum industry. Some of the benefits of these methods are: the inverted impedances are rock properties tightly calibrated with well data; the seismic inversion process itself attenuates the wavelet effects, reduces side-lobes and tuning effects and enables quantitative predictions of reservoir properties, all of which are advantages to improving the understanding of the reservoir geology and better management of the drilling programs. However, when the reservoir is below the resolution of the seismic and/or has thinner low permeability barrier layers (compartments), estimating the reservoir volume and/or evaluating the connectivity in the reservoir geobodies from deterministic seismic inversion become less accurate and in many cases unfeasible. In such cases, the geostatistical seismic inversion method provides more accurate reservoir volumes and the uncertainty associated with the 3D models can be assessed and quantified. For the work described in this paper, we used a stochastic inversion methodology, which simulates many possible scenarios, to better discriminate the thickness of the sand/shale layers and the areal extent the layers in the Oligocene reservoir in the Barracuda Field, Campos Basin, offshore Brazil.

It is often observed that common image gathers computed after FWI are not flat. This is due to an improper anisotropy estimation. We propose a new joint reflections-diving rays tomography to estimate anisotropy prior to FWI. It aims at both flattening the common image point gathers and matching the kinematics of the observed and synthetic diving waves. The method is successfully demonstrated on a real land 3D broadband dataset.

Standard prestack depth migration (PSDM), e.g., Kirchhoff/RTM, is by nature unable to fully recover the reflectivity with desired amplitude and resolution due to factors such as inhomogeneous subsurface illumination and irregular acquisition geometry. This shortcoming is well recognized by the imaging community and has propelled the re-emergence of least-squares migration (LSM) in recent years. Another factor that degrades amplitude fidelity and image resolution is the attenuation of seismic waves induced by anelastic absorption and elastic scattering during its propagation inside the earth, quantified by the so-called quality factor (Q). Absorption causes frequency-dependent amplitude decay, phase distortion, and resolution reduction. This effect can be compensated through an inverse Q prestack depth migration (QPSDM). QPSDM has become an effective solution for seismic imaging in areas where strong shallow absorption anomalies exist. However, the excessive noise often accompanying QPSDM poses a big challenge to its application. We propose a least-squares Q migration (LSQM) method that combines the benefits of both LSM and QPSDM to improve the amplitude fidelity and image resolution of seismic data. Using an OBC data set acquired over the Angelin gas field offshore Trinidad, we demonstrate that LSQM not only retains the full benefits of QPSDM while mitigating its issue of over-boosted noise but also compensates for inhomogeneous illumination caused by overburden velocity variations and irregular acquisition geometry that standard PSDM suffers, leading to a final product with higher resolution and improved amplitude fidelity.

Assessing the uncertainty on the structural information contained in seismic images is critical for risk analysis in reservoir delineation, reserve estimation, and well planning. We propose here an original approach aiming at assessing structural uncertainties associated to ray based tomography. While it has similarities with formerly published approaches it is based on the random generation of equi-probable tomographic models rather than on randomly sampling the a posteriori “probability density function”. Moreover it is associated with non-linear slope tomography which allows considering some non-linear aspects of the problem. We think these two aspects offers significant advantages in terms of efficiency and accuracy. In this paper we carefully review the concepts and definitions (in particular the notions of confidence region and error bars), and then present our approach and discuss its advantages. We finally present an application to a North Sea dataset where we estimate errors bars for a target horizon.

3500 km2 of marine data from West of Shetlands have been reprocessed using latest processing technologies available (including deghosting). This case study demonstrates the benefit to re-process conventionnal data (acquired in the 90's) to get a broadband result (better definition, larger spectrum).

The Intergovernmental Panel on Climate Change (IPCC) has just concluded its Sixth Assessment Report Cycle (AR6). The reports making up the AR6 provide the most comprehensive overview of the current state of climate change, its impacts and the adaptation and mitigation options that are available. Each word of the Summary for Policy Makers (SPMs) of these reports is approved by the world’s governments. The IPCC has been clear on the urgency that is required to meet the targets of the Paris Agreement. We have, as a global society, no more than seven years and probably only three years to start drastically reducing emissions of greenhouse gases. If we do not, global temperature change will reach the 1.5⁰C threshold after which climate change impacts will become increasingly more dangerous and difficult to adapt to. As well as the three major AR6 reports, three Special Reports were produced, including the Special Report on Climate Change and Land (SRCCL) which emphasised the use of the land as a key resource.

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