... reservoir evaluation upstream oil & gas vector correlation algorithm latent space vae reservoir property undrilled location representation artificial intelligence deep learning deep algorithm variation testing location spe reservoir evaluation & engineering producer variance...

... decision selection as countless alternatives are impossible to be enumerated in a decision model (such as the mean-variance model). In many practical applications, the decision criterion of well placement optimization is based on maximizing the risk-adjusted value (mean-variance optimization) to capture...

... ratio in the fine and coarse scales, respectively. reservoir geomechanics neural network structural geology training set scaling method hydraulic fracturing wellbore design heterogeneity reservoir characterization artificial intelligence drilling operation resolution variance...

... as the variance in these coefficient estimates (used to establish P10 and P90 curves). We were also able to map EUR for 25 years across the study area. Finally, the universal co–Kriging model was cross validated with a leave–one–out scheme, which showed significant, but not unreasonable, error in the decline...

... be developed that propagates the uncertainties of production forecasts and economic evaluations generated from a sector model to full field-scale quantities. One can simplify this formulation further so that the variance of a field-scale quantity is analytically expressed as the variance of the same single...

... reserves evaluation log-normal distribution assessment liquified natural gas gas monetization probabilistic estimate probabilistic addition block 1 block 2 Standard Deviation probabilistically gas resource lng project variance april 2008 arithmetic addition reservoir characterization...

... zonation has already been developed within the geologic model. However, the logic of zonation for individual wells has immediate utility for us. Simply put, Tester-man looked at the permeability data for a vertical well and grouped the permeabilities in such a way as to minimize the variance within each...

... that the random variable V (in our case, the logarithms of permeabilities) is a Gaussian random variable with mean = m . The variance is (1) σ 2 = E { V 2 } − m 2 , and the semivariogram is γ ( h ). Now, consider linear combinations of these random variables...

...Liyong Li; Hamdi A. Tchelepi Summary An inversion method for the integration of dynamic (pressure) data directly into statistical moment equations (SMEs) is presented. The method is demonstrated for incompressible flow in heterogeneous reservoirs. In addition to information about the mean, variance...

... variance spe reservoir evaluation & engineering estimates of resource in place resource in place estimate reservoir evaluation (4) m ( p ) = ( μ B ) r ∫ p r p 1 μ B d p . The dimensionless rate for gas flow then becomes (5) q D...

... testing decomposition variance aaaaaaaaaaa spe reservoir evaluation upstream oil & gas outlier pressure transient analysis aaa aberrant transient artificial intelligence model parameter aaaaaaaa reservoir parameter procedure flow rate pressure data drillstem testing reservoir...

... that amount or more. The horizontal lines labeled "Var." represent the width or "spread" of the distributions, typically defined as the variance or its square root, standard deviation. Fig. 1 Probability density functions for two wells showing the relationships among proved, possible, mean...

... with increasing sample support. Among these trends are an increasing mean, decreasing variance, and an increasing semivariogram range. The results also clearly indicate that the different scales of heterogeneity upscale differently, with the small-scale structure being preferentially filtered from the data while...

... features control overall flow behavior even when short-range variability composes most of the variance. The short-range heterogeneities produce local smearing of displacement fronts. Thus, these carbonate systems exhibit two main types of petrophysical heterogeneity: a high degree of short-range...

...J. W. Jennings, Jr. Summary A new log-calibration technique presented in this paper determines how much core-sample variance should be reproduced by a calibrated log. The linear calibration parameters are then calculated from this variance and the log and core-sample means. The method relies...

... into reservoir parameter information by using the petrophysical relationship. Information from the ordinary kriging of the reservoir parameter well logs and its corresponding kriging variance. Vshale values in the stratigraphic cells are estimated as a weighted average of these two pieces of information...

... structural uncertainty spe reservoir eval additional reserve structural uncertainty depth conversion variance The size of the structural high south of well P-1 was estimated: a high point of at least 22 m above the top depth at well P-6 was identified with a probability of 90%. Unfortunately...

... for the case of two maps, although it can be extended to handle more than two maps. Adopting the notation of Doyen et al ., 4 we use x i to represent a normally distributed property with zero mean and unit variance at grid cell i in a 3D discretized geologic model. Also, let z 1 ,i and z 2, i...

.... The result is a model that contains vertical variability driven by well logs and the vertical-variogram model and spatial variability driven by the seismic map and the areal-variogram model. The estimate z* ( u ) and kriging variance σ BK 2 ( u ) are (1) z * u − m = ∑ α...