Amplitude Versus Offset Modelling Research Articles

Bayesian Inversion of Time-Lapse Seismic AVO Data for Multimodal Reservoir Properties

We consider time-lapse seismic amplitude versus offset (AVO) inversion for the reservoir properties porosity and water saturation, with a focus on monitoring the evolution of water saturation in a dynamic setting of ongoing production. We operate in a Bayesian framework based on the integration of seismic AVO modeling and rock physics relations. One major challenge in the inversion of seismic data for reservoir properties is the multimodality of these properties. Fluid saturation is generally bimodal due to the gravity effect, and often distinctly so, with abrupt spatial mode transitions. The novelty of the proposed approach is the assumption of a selection Gaussian random field (S-GRF) for the prior spatial model of porosity and water saturation, which can represent the multimodal characteristics of these reservoir properties. The likelihood model is Gauss-linear and based on linearized seismic and rock physics models, which entails that the associated posterior model is also an S-GRF, with analytically assessable parameters. Hence, the posterior model is capable of representing multimodality and abrupt spatial mode transitions. Two realistic case studies are considered; the production of an oil reservoir in the North Sea, and the injection of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> into a subsurface potential CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> storage unit. Focus is on the movement of the oil–water-contacts along a vertical profile in the first case, and on the expansion of the CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> region in a cross section in the other, both of which can be inferred from the changes in the water saturations. The results are considered to be very encouraging and the proposed statistical formulation appears to be particularly well suited for fluid monitoring problems of the described type.

Amplitude versus offset (AVO) modelling and analysis for quantitative interpretation of porosity and saturation: A case study for Sawan gas field, middle Indus basin, Pakistan

&#x0D; &#x0D; &#x0D; Porosity and water saturation are among the important reservoir parameters in terms of reservoir monitoring which in turn are necessary for placement of future production wells. Amplitude versus offset (AVO) modelling and analysis technique have been utilized for quantitative interpretation of porosity and water saturation. For AVO modeling, Gassmann fluid substitution model in connection with 2D seismic angle gathers were used for reservoir monitoring over Sawan-4 and Sawan-7 wells. AVO modellingresults showed significant increase of density log by substituting gas by water, moderate increase of P-wave velocity and no change in S-wave velocity. Moreover, AVO modelling proved a strong relationship between porosity and reflection amplitude for all offset ranges. The class IV anomaly was evident for all porosities down to 5%. From fluid substitution modelling it is evidentthat changes in water saturation do not change the AVO effect significantly which further substantiate by investigation of pre-stack time migration (PSTM) gathers verifying the modelling results. The amplitude relationship between Sawan-4 and Sawan-7 is close to the modelled result. The amplitude and the AVO behaviour indicate lower porosity or significantly reduced sand thickness in the vicinity of Sawan-4 and Sawan-7 wells.&#x0D; &#x0D; &#x0D;

Time lapse (4D) and AVO analysis: A case study of Gullfaks field, Northern North Sea

A 4D seismic or time lapse survey has been used to investigate the amplitude versus offset (AVO) effects on seismic data in order to identify anomalies in the Gullfaks field for three different reservoir intervals namely the Tarbert, Cook and Statfjord reservoirs. Repeatability analysis has shown that the earlier seismic vintages are the most unreliable for amplitude anomaly analysis as normalised root-mean square (NRMS) values are greater than 50%. This is above the threshold of good and medium repeatability. Fluid substitution models show increases in both P-wave velocity and density for increasing water saturations with a maximum change of 7.33% in the P-wave velocity, and this is in line with predictions from previous work using the Biot - Gassman equations. AVO modelling for the top Tarbert Formation interface produced scenarios of increasing amplitudes with offset for the presence of hydrocarbons, which dim out with 100% brine saturation. This correlates to class III gas sands for different situations of varying Poisson’s ratio across an interface, which has been previously modelled. Two anomalies were identified with one being related to increasing pressure due to water injection correlating to poor permeability around injector well 34/10-B-33. The second anomaly is a case of potential unswept hydrocarbons that displayed a consistent bright spot throughout all of the seismic vintages (in-inlines and crosslines). AVO attribute analysis of this event produced a class II anomaly. However, when comparing near and far offset seismic data, dimming effect was observed producing contrasting evidence. The dimming offset is viewed to have been as a result of poor repeatability values at far offsets. The modelling of the fluid contents in the studied formations to conform to existing literatures justifies the efficacy of the method.

QUANTITATIVE ASSESSMENT OF FREE GAS BENEATH GAS HYDRATE STABILITY ZONE FROM PRESTACK SEISMIC DATA AND ROCK PHYSICS: A CASE OF HOLE NGHP01‐10A, KRISHNA‐GODAVARI BASIN, INDIA

AbstractThe distribution, saturation, and hydrate indicator of free gas beneath gas hydrate stability zone have been the difficulty in the research of gas hydrate. Amplitude versus offset (AVO) and rock physics can be used to quantitatively interpret free gas saturation. In this paper, seismic data, acquired at K‐G basin, India, are processed to preserve true amplitude information. Based on the logging data and horizon calibration, free gas saturation of NGHP01‐10A is assessed with effective medium theory and AVO modeling. It is found that free gas saturation is related to its distribution patterns. Assessments of free gas saturation in homogeneous and patchy distribution are 0.3%∼0.4% and 3%∼4%, respectively. The results show good agreement with the crossplot of Poisson's ratio which are calculated from the measurement at NGHP01‐10D. Free gas is further deduced to be homogeneous distribution on the base of dry frame Poisson's ratio with the saturation of 0.3%∼0.4%.

AVO Modeling for Coal Seam Prediction

Amplitude versus offset (AVO) technology is an important tool and a leading skill to predict the fluid in reservoir and to identify the lithology. AVO was mainly used to predict the hydrocarbon reservoir. Its application in coal AVO identification technology is just beginning. The key to AVO technology is how to make a reasonable model for interpretation, and then to analyze the actual prestack seismic data. The conventional AVO forward model is based on two-layer model, and it cannot reflect thin interbed reservoirs or inhomogeneous medium. In order to interpret prestack seismic response of interbed coal seams, we construct AVO models by using the full well log data, which we compute the accurate reflection coefficient with different incidence angle, pick up the AVO Intercept and Gradient (I&G) with different frequency, and map the I &G cross-plot. This paper takes Gaojiabu coal field data as an example to make AVO forward models and AVO I&G cross-plots. Based on AVO cross-plots, we established a coal discriminator to distinguish the coal seam, carbonaceous-mud and argillaceous-carbon. Furthermore, we compared the AVO I&G cross-plot made from well log model and two-layer model.

Amplitude-versus-offset modeling of the bottom simulating reflection associated with submarine gas hydrates

Velocity analysis of multi-channel seismic (MCS) data and amplitude-versus-offset (AVO) modeling provides an efficient way of identifying gas hydrate and free gas, and therefore the nature of the bottom-simulating reflector (BSR). Additionally, AVO modeling also yields estimates of the hydrate concentration and free gas saturation across the BSR in terms of velocity distribution. In the present study, we apply directivity correction in order to accentuate the AVO behavior. Modeling for AVO pattern of the observed BSR over the Kerala–Konkan Offshore Basin may provide the probable velocity distribution across the BSR and thereby infer whether hydrate or hydrate/free gas model governs the AVO observations. Initial results indicate the possible presence of free gas underlying the gas hydrates-saturated sediments in this region.

Application of AVO Analysis to Seismic Data for Detection of Gas below Methane Hydrate Stability Zone in Nankai Trough Area

Abstract. For the purpose of development of methane hydrate, occurring in the deep marine subsurface, as a resource, the most important issue is to understand the methane hydrate system (generation, migration and accumulation) as well as to delineate the methane hydrate reservoir properties. We have applied the Amplitude Versus Offset (AVO) analysis to the seismic data acquired in the Nankai Trough, offshore Japan, in order to confirm the occurrence of gas just below the methane hydrate‐bearing zone, assuming that gas will show a so‐called Class‐3 AVO response. Knowledge of the amount and occurrence of gas in the sediment below methane hydrate‐bearing zone is one of the keys to understand the methane hydrate system.We have utilized the qualitative analysis of AVO methodology to delineate how gas is located below the BSR, which is thought to be the reflection event from the interface between the methane hydrate‐bearing zone and the underlying gas‐bearing zone. In the region of MITI Nankai Trough Well PSW‐3, we observe two BSRs separated by 25 ms. After AVO modeling using well data, we applied AVO attribute analysis and attribute crossplot analysis to the seismic data. Finally we applied an offset‐amplitude analysis to CMP gather data at specific locations to confirm the results of AVO attribute analysis. The AVO analysis shows that there is very little gas located in the underlying sediment below methane hydrate‐bearing zone. This result supports the fact that we could not obtain any clear evidence of gas occurrence just below the methane hydrate‐bearing zone in the Nankai Trough well drilling.

Seismic velocity studies of a gas hydrate bottom‐simulating reflector on the northern Cascadia continental margin: Amplitude modeling and full waveform inversion

On the northern Cascadia subduction margin, the multichannel seismic amplitude‐versus‐offset (AVO) behavior of a bottom‐simulating reflector (BSR) suggests a P wave velocity change from high‐velocity hydrate‐bearing sediment to lower velocity sediment containing a small amount of free gas. The observed nonlinear AVO behavior, constant or slightly decreasing amplitudes at near‐to‐mid offsets and a large‐amplitude increase at far offsets, can be reproduced in the models if an S wave velocity enhancement is assumed as expected from hydrate cementation. The AVO behavior of the hydrate BSR is found not to be as useful as was earlier thought for determining the amount of free gas below the BSR. This is because Poisson's ratio change below the BSR due to gas is likely very small in high‐porosity unconsolidated sediments. The uncertainty in the models is large, as there is no reliable S wave velocity information for the sediments containing hydrate or gas. AVO modeling alone is not sufficient to distinguish different velocity models across the BSR. Our interpretation of the BSR amplitude behavior is that a P wave velocity increase above the BSR is the main cause of the BSR reflection amplitude increase at large incidence angles. Caution must be taken in applying AVO analysis, as little is known about S wave velocities. However, subtle differences in BSR amplitude behavior and reflection waveform can provide constraints through very careful full waveform inversion. A well‐defined reference velocity‐depth profile is also required to represent water‐saturated sediment unaffected by either hydrate or free gas. Using full waveform velocity inversion, a high‐resolution velocity model for the hydrate BSR has been derived. The best fit model for the seismic data near the Ocean Drilling Program (ODP) sites 889/890 consists of a high‐velocity zone above the BSR and a thin low‐velocity layer below, in agreement with the ODP downhole velocity data.

Seismic studies of a bottom simulating reflection related to gas hydrate beneath the continental margin of the Beaufort Sea

The upper continental margin of the Beaufort Sea, north of Alaska, is underlain by a strong bottom simulating reflection (BSR) that lies 300 to 700 m beneath the seafloor and corresponds to the phase boundary between interstitial water and natural gas below and solid gas hydrate above. BSRs of similar origin are common worldwide, where they are usually interpreted to mark the base of gas hydrate‐bearing clastic sediment, with or without underlying free gas in the sediment. Surprisingly little is known about the origin of these strong reflections. In this paper we analyze the contrasting physical properties which produce BSRs by comparing synthetic BSR amplitudes and waveforms for varying source‐receiver offsets with multichannel seismic reflection data across the well‐developed BSR of the Beaufort Sea. In order to discriminate whether free gas is present under the BSR or not, it was necessary to supplement near‐vertical incidence data with prestack offset data. The amplitude‐versus‐offset (AVO) analysis indicates that the BSR is produced mainly by the existence of free gas in the clastic sediments beneath the BSR. The zone of free gas is, based on vertical incidence synthetics, estimated to be thinner than 11–16 m. It is possibly thicker than 16 m if the gas concentration decreases with depth. Saturation of gas hydrate in the sediment above the BSR is tentatively estimated from the AVO modeling to be less than 10% of the pore volume.

AVO and Anisotropy from Logs and Walkaways

AVO (Amplitude Versus Offset) is the seismic technique used for mapping lithology, and modelling is an important step for successful AVO interpretations. Shear velocity measurements are essential, since AVO attempts to exploit the elastic (as opposed to acoustic) nature of seismic wave propagation. A property of seismic wave propagation not often considered is anisotropy. This is probable because the magnitude of the anisotropy has been difficult to measure, and its effect on AVO is not widely known. New technology is helping to improve AVO modelling. Dipole source shear logging tools can now measure very slow shear velocities, increasing the range of applicability of AVO, and new borehole seismic techniques can measure anisotropy. When integrated, these new measurements provide more detailed information about the elastic moduli that govern wave propagation, and bring the possiblity for greater reliability in AVO interpretation. The effect of anisotropy on AVO is found to be significant and may lead to misinterpretations of AVO anomalies.

Amplitude versus offset: a systems approach

The information contained in amplitude-versus-offset (AVO) effects can be extremely useful in exploration/development work. A specialized set of techniques and software, however, is needed to extract and use this information properly. A systems approach has been developed at Berrong Enterprises Ltd to accomplish this goal. The system has as major components (1) AVO feasibility studies, (2) proper AVO data processing, (3) AVO scanning, (4) detailed AVO modelling supported by well logs, and (5) elastic model inversions at prospective locations. Each of these areas is discussed from an application/results viewpoint with practical examples from actual development problems. These examples will demonstrate the degree of information that is available in proper AVO information processing. Interpretation techniques to use this information are also presented.