A rock physics strategy for quantifying uncertainty in common hydrocarbon indicators

Geophysics ◽  
1998 ◽  
Vol 63 (6) ◽  
pp. 1997-2008 ◽  
Author(s):  
Gary Mavko ◽  
Tapan Mukerji
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Pore Fluid ◽  
Fluid Substitution ◽  
Seismic Velocities ◽  
S Wave ◽  
Seismic Attribute ◽  
The Impact ◽  
Quantifying Uncertainty ◽  
Range Of Values

We present a strategy for quantifying uncertainties in rock physics interpretations by combining statistical techniques with deterministic rock physics relations derived from the laboratory and theory. A simple example combines Gassmann’s deterministic equation for fluid substitution with statistics inferred from log, core, and seismic data to detect hydrocarbons from observed seismic velocities. The formulation identifies the most likely pore fluid modulus corresponding to each observed seismic attribute and the uncertainty that arises because of natural variability in formation properties, in addition to the measurement uncertainties. We quantify the measure of information in terms of entropy and show the impact of additional data about S-wave velocity on the uncertainty of the hydrocarbon indicator. In some cases, noisy S data along with noisy P data can convey more information than perfect P data alone, while in other cases S data do not reduce the uncertainty. We apply the formulation to a well log example for detecting the most likely pore fluid and quantifying the associated uncertainty from observed sonic and density logs. The formulation offers a convenient way to implement deterministic fluid substitution equations in the realistic case when natural geologic variations cause the reference porosity and velocity to span a range of values.

Geostatistical Inversion in Carbonate Reservoirs to Map Reservoir Quality With High Predictability – A Case Study From Onshore Abu Dhabi

2021 ◽  
Author(s):  
S Al Naqbi ◽  
J Ahmed ◽  
J Vargas Rios ◽  
Y Utami ◽  
A Elila ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Pore Volume ◽  
Rock Physics ◽  
Static Model ◽  
Reservoir Quality ◽  
Reservoir Properties ◽  
Geostatistical Inversion ◽  
High Predictability ◽  
The Impact

Abstract The Thamama group of reservoirs consist of porous carbonates laminated with tight carbonates, with pronounced lateral heterogeneities in porosity, permeability, and reservoir thickness. The main objective of our study was mapping variations and reservoir quality prediction away from well control. As the reservoirs were thin and beyond seismic resolution, it was vital that the facies and porosity be mapped in high resolution, with a high predictability, for successful placement of horizontal wells for future development of the field. We established a unified workflow of geostatistical inversion and rock physics to characterize the reservoirs. Geostatistical inversion was run in static models that were converted from depth to time domain. A robust two-way velocity model was built to map the depth grid and its zones on the time seismic data. This ensured correct placement of the predicted high-resolution elastic attributes in the depth static model. Rock physics modeling and Bayesian classification were used to convert the elastic properties into porosity and lithology (static rock-type (SRT)), which were validated in blind wells and used to rank the multiple realizations. In the geostatistical pre-stack inversion, the elastic property prediction was constrained by the seismic data and controlled by variograms, probability distributions and a guide model. The deterministic inversion was used as a guide or prior model and served as a laterally varying mean. Initially, unconstrained inversion was tested by keeping all wells as blind and the predictions were optimized by updating the input parameters. The stochastic inversion results were also frequency filtered in several frequency bands, to understand the impact of seismic data and variograms on the prediction. Finally, 30 wells were used as input, to generate 80 realizations of P-impedance, S-impedance, Vp/Vs, and density. After converting back to depth, 30 additional blind wells were used to validate the predicted porosity, with a high correlation of more than 0.8. The realizations were ranked based on the porosity predictability in blind wells combined with the pore volume histograms. Realizations with high predictability and close to the P10, P50 and P90 cases (of pore volume) were selected for further use. Based on the rock physics analysis, the predicted lithology classes were associated with the geological rock-types (SRT) for incorporation in the static model. The study presents an innovative approach to successfully integrate geostatistical inversion and rock physics with static modeling. This workflow will generate seismically constrained high-resolution reservoir properties for thin reservoirs, such as porosity and lithology, which are seamlessly mapped in the depth domain for optimized development of the field. It will also account for the uncertainties in the reservoir model through the generation of multiple equiprobable realizations or scenarios.

Elastic reflectivity vectors and the geometry of intercept-gradient crossplots

2019 ◽  
Vol 38 (10) ◽  
pp. 762-769
Author(s):  
Patrick Connolly
Keyword(s):  
Elastic Property ◽  
Elastic Properties ◽  
Wave Velocity ◽  
Seismic Data ◽  
Measurement Errors ◽  
Rock Physics ◽  
P Wave ◽  
S Wave ◽  
P Wave Velocity ◽  
Well Log Analysis

Reflectivities of elastic properties can be expressed as a sum of the reflectivities of P-wave velocity, S-wave velocity, and density, as can the amplitude-variation-with-offset (AVO) parameters, intercept, gradient, and curvature. This common format allows elastic property reflectivities to be expressed as a sum of AVO parameters. Most AVO studies are conducted using a two-term approximation, so it is helpful to reduce the three-term expressions for elastic reflectivities to two by assuming a relationship between P-wave velocity and density. Reduced to two AVO components, elastic property reflectivities can be represented as vectors on intercept-gradient crossplots. Normalizing the lengths of the vectors allows them to serve as basis vectors such that the position of any point in intercept-gradient space can be inferred directly from changes in elastic properties. This provides a direct link between properties commonly used in rock physics and attributes that can be measured from seismic data. The theory is best exploited by constructing new seismic data sets from combinations of intercept and gradient data at various projection angles. Elastic property reflectivity theory can be transferred to the impedance domain to aid in the analysis of well data to help inform the choice of projection angles. Because of the effects of gradient measurement errors, seismic projection angles are unlikely to be the same as theoretical angles or angles derived from well-log analysis, so seismic data will need to be scanned through a range of angles to find the optimum.

Gassmann fluid substitutions: A tutorial

Geophysics ◽  
2003 ◽  
Vol 68 (2) ◽  
pp. 430-440 ◽  
Author(s):  
Tad M. Smith ◽  
Carl H. Sondergeld ◽  
Chandra S. Rai
Keyword(s):  
Bulk Modulus ◽  
Bulk Density ◽  
Pore Fluid ◽  
Direct Result ◽  
Fluid Substitution ◽  
Seismic Attribute ◽  
Amplitude Variation With Offset ◽  
Effective Bulk Modulus ◽  
Porous Frame ◽  
Insight Into

Fluid substitution is an important part of seismic attribute work, because it provides the interpreter with a tool for modeling and quantifying the various fluid scenarios which might give rise to an observed amplitude variation with offset (AVO) or 4D response. The most commonly used technique for doing this involves the application of Gassmann's equations. Modeling the changes from one fluid type to another requires that the effects of the starting fluid first be removed prior to modeling the new fluid. In practice, the rock is drained of its initial pore fluid, and the moduli (bulk and shear) and bulk density of the porous frame are calculated. Once the porous frame properties are properly determined, the rock is saturated with the new pore fluid, and the new effective bulk modulus and density are calculated. A direct result of Gassmann's equations is that the shear modulus for an isotropic material is independent of pore fluid, and therefore remains constant during the fluid substitution process. In the case of disconnected or cracklike pores, however, this assumption may be violated. Once the values for the new effective bulk modulus and bulk density are calculated, it is possible to calculate the compressional and shear velocities for the new fluid conditions. There are other approaches to fluid substitution (empirical and heuristic) which avoid the porous frame calculations but, as described in this tutorial, often do not yield reliable results. This tutorial provides the reader with a recipe for performing fluid substitutions, as well as insight into why and when the approach may fail.

Seismic attribute enhancement of weak and discontinuous gas hydrate bottom-simulating reflectors in the Pegasus Basin, New Zealand

2019 ◽  
Vol 7 (3) ◽  
pp. SG11-SG22 ◽  
Author(s):  
Heather Bedle
Keyword(s):  
New Zealand ◽  
Gas Hydrates ◽  
Gas Hydrate ◽  
Seismic Data ◽  
Rock Physics ◽  
Stability Zone ◽  
Seismic Attribute ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Hydrate Stability

Gas hydrates in the oceanic subsurface are often difficult to image with reflection seismic data, particularly when the strata run parallel to the seafloor and in regions that lack the presence of a bottom-simulating reflector (BSR). To address and understand these imaging complications, rock-physics modeling and seismic attribute analysis are performed on modern 2D lines in the Pegasus Basin in New Zealand, where the BSR is not continuously imaged. Based on rock-physics and seismic analyses, several seismic attribute methods identify weak BSR reflections, with the far-angle stack data being particularly effective. Rock modeling results demonstrate that far-offset seismic data are critical in improving the imaging and interpretation of the base of the gas hydrate stability zone. The rock-physics modeling results are applied to the Pegasus 2009 2D data set that reveals a very weak seismic reflection at the base of the hydrates in the far-angle stack. This often-discontinuous reflection is significantly weaker in amplitude than typical BSRs associated with hydrates. These weak far-angle stack BSRs often do not appear clearly in full stack data, the most commonly interpreted seismic data type. Additional amplitude variation with angle (AVA) attribute analyses provide insight into identifying the presence of gas hydrates in regions lacking a strong BSR. Although dozens of seismic attributes were investigated for their ability to reveal weak reflections at the base of the gas hydrate stability zone, those that enhance class 2 AVA anomalies were most effective, particularly the seismic fluid factor attribute.

Sediments with gas hydrates: Internal structure from seismic AVO

Geophysics ◽  
1998 ◽  
Vol 63 (5) ◽  
pp. 1659-1669 ◽  
Author(s):  
Christine Ecker ◽  
Jack Dvorkin ◽  
Amos Nur
Keyword(s):  
Internal Structure ◽  
Wave Velocity ◽  
Seismic Data ◽  
Pore Space ◽  
Rock Physics ◽  
P Wave ◽  
S Wave ◽  
High Concentration ◽  
Amplitude Variation With Offset ◽  
Bottom Simulating Reflector

We interpret amplitude variation with offset (AVO) data from a bottom simulating reflector (BSR) offshore Florida by using rock‐physics‐based synthetic seismic models. A previously conducted velocity and AVO analysis of the in‐situ seismic data showed that the BSR separates hydrate‐bearing sediments from sediments containing free methane. The amplitude at the BSR are increasingly negative with increasing offset. This behavior was explained by P-wave velocity above the BSR being larger than that below the BSR, and S-wave velocity above the BSR being smaller than that below the BSR. We use these AVO and velocity results to infer the internal structure of the hydrated sediment. To do so, we examine two micromechanical models that correspond to the two extreme cases of hydrate deposition in the pore space: (1) the hydrate cements grain contacts and strongly reinforces the sediment, and (2) the hydrate is located away from grain contacts and does not affect the stiffness of the sediment frame. Only the second model can qualitatively reproduce the observed AVO response. Thus inferred internal structure of the hydrate‐bearing sediment means that (1) the sediment above the BSR is uncemented and, thereby, mechanically weak, and (2) its permeability is very low because the hydrate clogs large pore‐space conduits. The latter explains why free gas is trapped underneath the BSR. The seismic data also indicate the absence of strong reflections at the top of the hydrate layer. This fact suggests that the high concentration of hydrates in the sediment just above the BSR gradually decreases with decreasing depth. This effect is consistent with the fact that the low‐permeability hydrated sediments above the BSR prevent free methane from migrating upwards.

Geopressure prediction using seismic data: Current status and the road ahead

Geophysics ◽  
2002 ◽  
Vol 67 (6) ◽  
pp. 2012-2041 ◽  
Author(s):  
N. C. Dutta
Keyword(s):  
Seismic Data ◽  
Large Scale ◽  
Best Practice ◽  
Seismic Velocity ◽  
Rock Physics ◽  
Rock Properties ◽  
Current Status ◽  
Seismic Attribute ◽  
Attribute Analysis ◽  
Exploration And Production

The subject of seismic detection of abnormally high‐pressured formations has received a great deal of attention in exploration and production geophysics because of increasing exploration and production activities in frontier areas (such as the deepwater) and a need to lower cost without compromising safety and environment, and manage risk and uncertainty associated with very expensive drilling. The purpose of this review is to capture the “best practice” in this highly specialized discipline and document it. Pressure prediction from seismic data is based on fundamentals of science, especially those of rock physics and seismic attribute analysis. Nonetheless, since the first seismic application in the 1960s, practitioners of the technology have relied increasingly on empiricism, and the fundamental limitations of the tools applied to detect such hazardous formations were lost. The most successful approach to seismic pressure prediction is one that combines a good understanding of rock properties of subsurface formations with the best practice for seismic velocity analysis appropriate for rock physics applications, not for stacking purposes. With the step change that the industry has seen in the application of the modern digital computing technology to solving large‐scale exploration and production problems using seismic data, the detection of pressured formations can now be made with more confidence and better resolution. The challenge of the future is to break the communication and the “language barrier” that still exists between the seismologists, the rock physicists, and the drilling community.

Diagenesis-driven pore fluid discrimination

2019 ◽  
Vol 38 (5) ◽  
pp. 366-373 ◽  
Author(s):  
Jack Dvorkin
Keyword(s):  
Acoustic Impedance ◽  
Rock Physics ◽  
Pore Fluid ◽  
Oil Field ◽  
Poisson's Ratio ◽  
Rock Properties ◽  
Fluid Substitution ◽  
Diagenetic Processes ◽  
Medium Porosity ◽  
Fluid Detection

In order to determine a direct hydrocarbon indicator in an oil field formed by low- to medium-porosity fast sandstone, we examine wireline data from four wells. Fluid substitution indicates that the sensitivity of the acoustic impedance and Poisson's ratio to oil-to-brine changes is very small. It appears, however, that due to diagenetic processes, the porosity in the brine-filled strata is noticeably smaller than that in the oil-saturated intervals. This porosity difference makes the impedance in the presence of oil noticeably smaller than that where brine is present. The respective impedance cutoff can serve as a discriminator for fluid detection in the seismically derived acoustic impedance volumes. The lesson learned is that merely relying on a rock-physics tool, such as fluid substitution, may not necessarily provide a fluid-detection recipe. Instead, we need to examine a plethora of natural events that may affect rock properties and then translate these effects into seismically detectable variables.

Predicting reservoir quality in the Bakken Formation, North Dakota, using petrophysics and 3C seismic data

2020 ◽  
Vol 8 (4) ◽  
pp. T851-T868
Author(s):  
Andrea G. Paris ◽  
Robert R. Stewart
Keyword(s):  
North Dakota ◽  
Seismic Data ◽  
Rock Physics ◽  
P Wave ◽  
Well Logs ◽  
Reservoir Quality ◽  
Bakken Formation ◽  
S Wave ◽  
Converted Wave ◽  
Wave Velocities

Combining rock-property analysis with multicomponent seismic imaging can be an effective approach for reservoir quality prediction in the Bakken Formation, North Dakota. The hydrocarbon potential of shale is indicated on well logs by low density, high gamma-ray response, low compressional-wave (P-wave) and shear-wave (S-wave) velocities, and high neutron porosity. We have recognized the shale intervals by cross plotting sonic velocities versus density. Intervals with total organic carbon (TOC) content higher than 10 wt% deviate from lower TOC regions in the density domain and exhibit slightly lower velocities and densities (<2.30 g/cm3). We consider TOC to be the principal factor affecting changes in the density and P- and S-wave velocities in the Bakken shales, where VP/ VS ranges between 1.65 and 1.75. We generate the synthetic seismic data using an anisotropic version of the Zoeppritz equations, including estimated Thomsen’s parameters. For the tops of the Upper and Lower Bakken, the amplitude shows a negative intercept and a positive gradient, which corresponds to an amplitude variation with offset of class IV. The P-impedance error decreases by 14% when incorporating the converted-wave information in the inversion process. A statistical approach using multiattribute analysis and neural networks delimits the zones of interest in terms of P-impedance, density, TOC content, and brittleness. The inverted and predicted results show reasonable correlations with the original well logs. The integration of well log analysis, rock physics, seismic modeling, constrained inversions, and statistical predictions contributes to identifying the areas of highest reservoir quality within the Bakken Formation.

scholarly journals Rock Physics Based Interpretation of Seismically Derived Elastic Volumes

2021 ◽  
Vol 8 ◽  
Author(s):  
Abrar Alabbad ◽  
Jack Dvorkin ◽  
Yazeed Altowairqi ◽  
Zhou F. Duan
Keyword(s):  
Water Saturation ◽  
Rock Physics ◽  
Clay Content ◽  
Total Porosity ◽  
Pore Fluid ◽  
Oil Field ◽  
Rock Properties ◽  
Volumetric Properties ◽  
S Wave ◽  
A Site

A rock physics based seismic interpretation workflow has been developed to extract volumetric rock properties from seismically derived P- and S-wave impedances, Ip and Is. This workflow was first tested on a classic rock physics velocity-porosity model. Next, it was applied to two case studies: a carbonate and a clastic oil field. In each case study, we established rock physics models that accurately relate elastic properties to the rock’s volumetric properties, mainly the total porosity, clay content, and pore fluid. To resolve all three volumetric properties from only two inputs, Ip and Is, a site-specific geology driven relation between the pore fluid and porosity was derived as a hydrocarbon identifier. In order to apply this method at the seismic spatial scale, we created a coarse-scale elastic and volumetric variables by using mathematical upscaling at the wells. By using Ip and Is thus upscaled, we arrived at the accurate interpretation of the upscaled porosity, mineralogy, and water saturation both at the wells and in a simulated vertical impedance section generated by interpolation between the wells.

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