Hydrate Composition from Seismic Data: An Inverse Procedure

Self Cite

The influence of two fundamentally different types of uncertainty on the value of oil field production are investigated here. First considered is the uncertainty caused by the fact that the expected value estimate is not one of the possible outcomes. To correctly allow for the risk attendant upon using the expected value as a measure of worth, even with statistically sharp parameters, one needs to incorporate the uncertainty of the expected value. Using a simple example we show how such incorporation allows for a clear determination of the relative risk of projects that may have the same expected value but very different risks. We also show how each project can be risked on its own using the expected value and variance. This uncertainty type is due to the possible pathways for different outcomes even when parameters categorizing the system are taken to be known. Second considered is the risk due to the fact that parameters in oil field estimates are just estimates and, as such, have their own intrinsic errors that influence the possible outcomes and make them less certain. This sort of risk depends upon the uncertainty of each parameter, and also the type of distribution the parameters are taken to be drawn from. In addition, not all uncertainties in parameter values are of equal importance in influencing an outcome probability. We show how can determine the relative importance for the parameters and so determine where to place effort to resolve the dominant contributions to risk if it is possible to do so. Considerations of whether to acquire new information, and also whether to undertake further studies under such an uncertain environment, are used as vehicles to address these concerns of risk due to uncertainty. In general, an oil filed development project has to contend with all the above types of risk and uncertainty. It is therefore of importance to have quantitative measures of risk so that one can compare and contrast the various effects, and so that corporate decision-makers can use the information in a rational manner as they seek to enhance corporate profit. This paper provides such methods and measures of assessing risk.

Section: B Uncertainty Of Parametersmentioning

confidence: 99%

Section: B Uncertainty Of Parametersmentioning

confidence: 99%

Value Change in Oil and Gas Production: V. Incorporation of Uncertainties and Determination of Relative Importance

Lerche

Noeth

2002

Self Cite

“…There are two kinds of methods used for inverting elastic and petrophysical parameters, namely the two-step inversion and joint inversion methods (Bosch et al, 2010). Two-step inversion first employs seismic inversion to obtain the elastic parameters and then estimates the petrophysical parameters, through the deterministic or statistical relationship between the elastic and petrophysical parameters (Avseth et al, 2001;Buland et al, 2008;Grana and Della Rossa, 2010;Lerche and Noeth, 2002;Lessenger and Cross, 1996;Mukerji et al, 2001;Saltzer et al, 2005;Thomsen and Noeth, 2001). Due to the complexity and variability of reservoirs, the elastic parameters of different lithologies often have some range of overlap, and the error caused by the inversion of elastic parameters may lead to further error in estimations of reservoir petrophysical parameters.…”

Section: Introductionmentioning

confidence: 99%

Prestack seismic facies-controlled joint inversion of reservoir elastic and petrophysical parameters for sweet spot prediction

Sheng

Huang

Li³

et al. 2017

The elastic and petrophysical parameters of a reservoir can be directly applied to lithology prediction and fluid identification. Existing seismic joint inversion methods for estimating the elastic and petrophysical parameters of a reservoir are primarily based on either the Gassmann equation, with which these parameters are inverted from prestack seismic data through stochastic optimization methods, or Wyllie's modified equation, with which these parameters are inverted from poststack seismic data using deterministic optimization methods. Regardless of the stochastic or deterministic inversion of reservoir parameters, only the microscopic connection between parameters is considered, without considering the constraints of the macroscopic geological background. The purpose of this work is to develop a strategy for estimating the elastic and petrophysical parameters of a reservoir based on the Gassmann equation and seismic facies using deterministic seismic prestack inversion. We employ the Gassmann equation to construct the relationship between the prestack seismic data and petrophysical parameters and use a low-pass filter matrix to obtain the combination of seismic facies and seismic inversion and improve the robustness of the inversion results. We treat the joint posterior probability of elastic and petrophysical parameters as the objective function under a Bayesian framework by expanding the objective function with the Taylor formula; the joint equations composed of elastic and petrophysical parameters are obtained through differentiation. The conjugate gradient method

“…Seismic analysis have an important role in the assessment of hydrate resources in these regions, while direct measurements (e.g. well information) are rare or absent (Lerche and Noeth, 2002;Pevzner et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Compressional and Shear Wave Pre-Stack Analysis of Gas-Hydrate Resources in the Makran Accretionary Prism

Salehi

Javaherian

Pour

et al. 2014

The method of amplitude-variation-with-angle (AVA) inversion was applied to estimate hydrate and gas saturations along bottom simulating reflector (BSR) at the Makran Accretionary Prism, Iran. Two AVA approximations were used to calculate their corresponding AVA attributes were compared to each other. To investigate the accuracy of each attribute and measure its probable deviation from actual values, the AVA inversion was first applied to 30 sets of synthetic models. Synthetic results showed that uncertainties involved in two input parameters of inversion, density/velocity relation and Vs/Vp, causes an error in the estimated attributes which result in unrealistic estimations of hydrate and gas saturations. To diminish the adverse impact of this uncertainty on the field data, a simulated annealing (SA) approach was suggested to derive the Vs/Vp in a number of locations. These values were then interpolated to provide a model of estimated Vs/Vp in other locations. Quantification of hydrate and gas saturations was based on a correlation between the AVA inverted attributes and the rock physics derived attributes (RPDA). The RPDAs were generated using the effective medium theory (EMT) rock physics modeling applied on models with assumed input parameters such as lithology and porosity. Corresponding saturations of the nearest RPDA to a pair of AVA inverted attributes were considered as the hydrate and gas saturation for that specific location. The quantification assessment indicated 11%, 14% and 15% hydrate saturations in the vicinity of the BSR for locations with low, intermediate and high poststacked amplitude, respectively. The saturation of the free gas was also estimated as less than 1%, 2% and 3% with the same order as above, respectively.