Mita Sengupta

Uncertainty in seismic-based pay volume estimation Analysis using rock physics and Bayesian statistics

Pay volume (PV) is a measure of the amount of hydrocarbon volume or pay in the overall reservoir and is used to appraise reservoir quality and the economics associated with reservoir development. As seismic-based reservoir characterization technology is advancing, lithology and porosity information derived from seismic inversion is often used to derive an estimate of PV. In general, PV estimation from seismic data is based on some sort of rock physics transformation and seismic inversion algorithm, and both may be nonunique. As PV estimates are often used for economic decision making, it is important to associate expected risk or confidence associated with the prediction. This article presents a workflow to compute quantitative estimates of PV, along with associated uncertainties, from well-log calibrated prestack seismic inversion attributes. The main tools used in this workflow are seismic (AVO) inversion, rock physics, and Bayesian statistics. The PV is estimated from the seismically derived rock prope...

From pore-pressure prediction to reservoir characterization: A combined geomechanics-seismic inversion workflow using trend-kriging techniques in a deepwater basin

To optimize drilling decisions and well planning in overpressured areas, it is essential to carry out pore-pressure predictions before drilling. Knowledge of pore pressure implies knowledge of the effective stress, which is a key input for several geomechanics applications, such as fault slip and fault seal analysis and reservoir compaction studies. It is also a required input for 3D and 4D seismic reservoir characterization. Because the seismic response of shales and sand depends on their compaction history, the effective stress will govern the sedimentary seismic response. This is in contrast to normally pressured regimes, where the depth below mudline (or overburden stress) is typically used to characterize the compaction effect.

Relationship between velocity and anisotropy perturbations and anomalous stress field around salt bodies

Due to a growing demand for new hydrocarbon reservoirs, subsalt exploration has increased significantly during this decade. Subsalt imaging and drilling pose many challenges and opportunities, and subsalt velocity estimation is crucial for both tasks.

Using Geomechanical Modeling And Wide-azimuth Data to Quantify Stress Effects And Anisotropy Near Salt Bodies In the Gulf of Mexico

The state of stress in the subsurface influences the seismic velocity observed, and therefore, velocity may be used to indicate the state of stress in the subsurface. The presence of salt in a sedimentary column has a profound effect on the state of stress in the subsurface due to the difference in body forces (buoyancy effect) and abrupt material property changes, together with complex geometry, which is often associated with salt bodies. As a result of the presence of the salt body, the stress field around the salt is perturbed and is expected to affect the velocity field.

Building a Seismic-driven 3D Geomechanical Model In a Deep Carbonate Reservoir

In this paper we show how to extend seismic-driven earth model building into the domain of geomechanics and drilling. A mechanical earth model (MEM) is a quantitative description of rock mechanical properties and in-situ stresses in the subsurface. Formation strength and in-situ stress are key components that impact well design. Most mechanical earth models, even today, are one-dimensional (1D), based on well and drilling data alone. The concept of using seismically derived horizons and velocities to extend the MEM into 3D space was introduced a few years ago. Very recently, a few authors have demonstrated the power of seismic inversion to improve the resolution and quality of a 3D MEM. We present a case-study from Kuwait (Sabriyah field) where a 3D geomechanical model was built using a combination of wellbore geomechanics, geologic structure, and seismic inversion-derived lithofacies and elastic properties. We show critical challenges facing seismic-based geomechanical model-building, demonstrate current solutions, and discuss future strategies.

Velocity Updating Around Salt Bodies Using Stress Modeling Solutions And Non-linear Elasticity

Subsalt imaging and velocity estimation is usually a challenging task. Initial estimates of subsalt velocity often come from regional depth trends that ignore reduction in effective stress below salt, and therefore over predict subsalt velocities. We present a geomechanically constrained approach to improve velocity estimation around salt bodies using stress-strain modeling and nonlinear elasticity. Computing the full stress tensor field using 3D numerical modeling, we observe (a) significant reduction in effective stress below salt bodies, and (b) anisotropic solutions for the stress and strain tensors near the edges of the salt body. Not only are these observations expected from the basic laws of physics, but they have also been observed in reality in the form of velocity reduction below salt and velocity anisotropy near salt bodies. Our strategy is to incorporate this expected behavior into the seismic imaging workflow for improved velocity model building. Other potential applications of this method can include reservoir characterization and drilling hazard identification.

High-resolution Fracture Characterization From Azimuthal P-wave Seismic Surveys Using Rock Physics Model-based Bayesian Inversion — Pinedale Field, Wyoming

Summary Estimation of reservoir properties such as porosity, clay content, hydrocarbon pore-volume, and fracture intensity is the main objective of most reservoir characterization studies. Interval data, such as impedance, velocity, and density, are better suited to rock property estimation when compared to interface data such as reflectivity and gradient. We characterize a fractured reservoir by quantitative interpretation of seismic interval (or layer) properties. We use three seismic inversion attributes: P-impedance (IP), Simpedance (IS), and an anisotropy attribute Γ (a function the anisotropic parameters ) (V δ and γ) obtained by integrating data from kinematics (traveltimes) and dynamics (reflectivities). Ultimately, we use rock physics model-based Bayesian inversion to derive porosity and crack density estimates along with their associated

Effective -stress-based reservoir characterization in an offshore basin

Summary Effective stress is a key attribute that enables the prediction of subsurface lithology units in overpressured basins. Because the seismic response of shales and sand depends on their compaction history, the effective stress will govern the sedimentary seismic response. This is in contrast to normally pressured regimes, where the depth below mudline (or overburden stress) is typically used to characterize the compaction effect. Effective stress enables one to map nonstationary sedimentary compaction in space. We use seismically derived effective stress as an additional attribute in the Bayesian Lithofacies classification (Bachrach et al., 2004; Mukerji et al., 2001). The other attributes in the process are elastic parameters such as acoustic and shear impedances and density obtained from the multiattribute seismic inversion (Roberts et al., 2005). The modified reservoir characterization method has been applied to a deepwater basin that contains reservoir units of the Pleistocene to the mid-Miocene age extending over a large area and is known to contain overpressure zones.

Stress induced seismic velocity anisotropy study

A method of calculating stress induced seismic velocity anisotropy is presented and studied. In a first step an effective stiffness tensor of a stressed rock is calculated using third order elasticity (TOE) theory. In a second step, anisotropic seismic Pand S-wave velocities are calculated from the effective stiffness tensor using Tsvankin’s notation, which is an extension of Thomsen’s weak anisotropy notation. This method is used to study anisotropic velocity changes due to changes in hydrostatic, uni-axial and tri-axial stress using stress-sensitivity (thirdorder) parameters given in the literature. The computations show, in agreement with experimental observations, that the strongest P-wave velocity increases are observed in the direction of the largest stress increase. For S-waves, the largest velocity increase is observed for S-waves polarized parallel to the direction of maximum stress-increase. Therefore, S-waves have the potential to be used as a tool to monitor horizontal stress changes.

Quantifying Reservoir Properties of the East Texas Woodbine Through Rock Physics And Multiattribute Seismic Inversion

Multiattribute seismic inversion (MASI) is a newly developed prestack inversion technique for inverting seismic data into elastic parameter attributes. The method integrates several major technologies (Roberts, et al., 2005), including AVO processing and analysis, well-log editing and calibration, prestack waveform inversion (PSWI) (Mallick, et al., 2000), wavelet processing, and poststack inversion. The outputs are high-resolution absolute acoustic and shear impedance and density volumes consistent with the seismic data and the well-log data. The inverted elastic parameter volumes are used for detailed interpretation of lithofacies and pore-fluid content in the subsurface. Combined with rock physics modeling and rock property mapping through LithoCube (Bachrach, et al., 2004) and joint porosity-saturation inversion (Bachrach and Dutta, 2004), the method provides a powerful tool for quantitative reservoir description and characterization. The results are the most-probable litho-class, porosity, and saturation with uncertainties of prediction at every sample point in the 3-D volume. Recently we applied this method to identify and characterize Woodbine sandstone reservoirs in an onshore East Texas field.