Chris Finn

Spectral-decomposition response to reservoir fluids from a deepwater West Africa reservoir

Westudythespectral-decompositionresponsetoreservoir fluids from a deepwater WestAfrica reservoir through a systematicmodelingapproach.Ourworkflowstartsfromselecting the seismic data far-angle seismic images that show morepronouncedfluideffectbasedonamplitude-versus-offsetAVOanalysis.Syntheticseismicforwardmodelingperformed at the control well established the quality of the seismicwelltie.Reservoirwedgemodeling,spectraldecomposition of the field and synthetic seismic data, and theoretical analyses were conducted to understand the spectral-decomposition responses. The reservoir fluid type is a main factor controlling the spectral response. For this deepwater reservoir, the amplitude contrast between oil sand and brine sand is higher at low frequencies 15 Hz. In addition, synthetic modeling can help identify the possible frequency band where the amplitude contrast between hydrocarbon sand and brinesandishigher.Whenproperlyincludedinacomprehensive direct-hydrocarbon-indicator DHI‐AVO evaluation, spectral decomposition can enhance the identification of hydrocarbons.

Deterministic Spectral Balancing For High-Fidelity AVO

A common problem for Amplitude Versus Offset (AVO) analysis is that the frequency spectra of near and far-offset data are different; near-offset sections contain more highfrequency energy than far-offset sections do. This variation in frequency content can significantly alter the AVO response, since the interference patterns between adjacent reflection events (for instance the top and bottom of a reservoir layer) will be different on the near and far-offset data. Difference in interference (tuning) can create false AVO anomalies or make valid AVO anomalies disappear. Because of their different frequency spectra, correlating reflection events between near and far-offset sections is sometimes difficult or ambiguous.

Predicting Vshale and porosity using cascaded seismic and rock physics inversion

In characterizing hydrocarbon reservoirs, estimating reserves, and developing models for how to best extract the hydrocarbons, it is useful to know the lithology and associated porosity of the rocks in the target interval. It is particularly important to have accurate estimates of volumes and flow characteristics early in the development of deepwater reservoirs, as sizing the facilities to the resource is a significant component of an economically successful project. Accurate characterization of the reservoir can be a challenge in this situation as well control is typically very limited.

Spectral Decomposition Response to Reservoir Fluids From a Deepwater Reservoir

Summary Spectral decomposition of far-angle stack seismic data from a deepwater reservoir revealed that the oil leg has anomalously high amplitude at low frequencies. A systematic seismic forward modeling study was performed to understand the empirical observations and to identify the primary physical mechanisms. The first part of the study used a wedge model of reservoir sand containing different fluids (gas/oil/water) encased in shale. The second part of the study employed a stochastic simulation approach in which the properties and stacking pattern of the modeled reservoir interval were perturbed to span the range observed in the logs from wells that penetrated this reservoir. Each synthetic seismogram was spectrally decomposed and analyzed.

What's important in making far-stack well-to-seismic ties in West Africa?

Seismic-to-well ties are a fundamental part of seismic data analysis. In both processing and interpretation, well ties provide an important link between the data we record and the physics we believe to be occurring. Understanding the relationship between the reservoir and fluid properties measured in the wells and their expression in the seismic data is crucial to making predictions of reservoir and fluid properties away from the well based on the seismic response.

Lithofacies Prediction in Clastic Deep Water Reservoirs

Summary We use core and log relationships to systematically predict deep water lithofacies from seismic derivative volumes. We exploit seismic inversions that solve for Vshale and porosity simultaneously, the two key components that are needed to predict lithofacies. Fluid contacts and anisotropy are accounted for in the inversion, and the low frequency models that are used to invert for Vshale and porosity are based on geologic data and interpretation. The low frequency information plays a significant role in obtaining accurate lithofacies predictions. The lithofacies predictions allow us to identify areas that have high concentration turbidites, low concentration turbidites, poorly sorted traction beds, and shales within the reservoir. The lithofacies predictions have provided a basis for modifying geologic concepts and interpretations and they have enhanced the understanding of channel architecture, environment of deposition, and reservoir connectivity.

Stress-induced Velocity Anisotropy of Unconsolidated Sand Under Realistic Reservoir Stress Conditions

Ultrasonic velocity measurements were made on dry and oil saturated samples/cores of unconsolidated sands to investigate the stress-induced velocity anisotropy under realistic reservoir stress conditions. Instrumentation was arranged to simultaneously measure five velocities (axial P, axial S, radial P, radial S polarized radially, and radial S polarized axially) and the axial and radial deformation of the samples in a single run.

Using linear combinations of angle stacks to predict band‐limited porosity and vshale

We introduce a new type of weighted stack that can be applied to seismic data to produce band-limited estimates of porosity and shale volume (vshale). This weighted stack combines linearized approximations of the seismic reflectivity with linear rock property relationships into a single linear operator that can be applied directly to angle stacks. In order to account for the effects of fluid property variations in the rock property relationships, we specify the location of the fluid contacts within a reservoir and apply rock property relationships appropriate to the fluid at each point in the seismic volume. This method can be useful after an exploration well has been drilled and well logs are available for developing rock property relationships and fluid contact information is available. The method can then be applied to define the distribution of reservoir quality (sands) as part of development planning.