Conditioning Model Ensembles to Various Observed Data (Field and Regional Level) by Applying Machine-Learning-Augmented Workflows to a Mature Field with 70 Years of Production History

Summary Forecasting production from hydrocarbon fields is challenging because of the large number of uncertain model parameters and the multitude of observed data that are measured. The large number of model parameters leads to uncertainty in the production forecast from hydrocarbon fields. Changing operating conditions [e.g., implementation of improved oil recovery or enhanced oil recovery (EOR)] results in model parameters becoming sensitive in the forecast that were not sensitive during the production history. Hence, simulation approaches need to be able to address uncertainty in model parameters as well as conditioning numerical models to a multitude of different observed data. Sampling from distributions of various geological and dynamic parameters allows for the generation of an ensemble of numerical models that could be falsified using principal-component analysis (PCA) for different observed data. If the numerical models are not falsified, machine-learning (ML) approaches can be used to generate a large set of parameter combinations that can be conditioned to the different observed data. The data conditioning is followed by a final step ensuring that parameter interactions are covered. The methodology was applied to a sandstone oil reservoir with more than 70 years of production history containing dozens of wells. The resulting ensemble of numerical models is conditioned to all observed data. Furthermore, the resulting posterior-model parameter distributions are only modified from the prior-model parameter distributions if the observed data are informative for the model parameters. Hence, changes in operating conditions can be forecast under uncertainty, which is essential if nonsensitive parameters in the history are sensitive in the forecast.

Best-fit probability distribution models for monthly rainfall of Northeastern Brazil

The analysis of precipitation data is extremely important for strategic planning and decision-making in various natural systems, as well as in planning and preparing for a drought period. The drought is responsible for several impacts on the economy of Northeast Brazil (NEB), mainly in the agricultural and livestock sectors. This study analyzed the fit of 2-parameter distributions gamma (GAM), log-normal (LNORM), Weibull (WEI), generalized Pareto (GP), Gumbel (GUM) and normal (NORM) to monthly precipitation data from 293 rainfall stations across NEB, in the period 1988–2017. The maximum likelihood (ML) method was used to estimate the parameters to fit the models and the selection of the model was based on a modification of the Shapiro-Wilk statistic. The results showed the chosen 2-parameter distributions to be flexible enough to describe the studied monthly precipitation data. The GAM and WEI models showed the overall best fits, but the LNORM and GP models gave the best fits in certain months of the year and regions that differed from the others in terms of their average precipitation.

A Comparative Study on Probabaility Analysis of Annual Maximum Daily Streamflow Using Conventional and L-Moments

The study aims at the probabilistic analysis of annual maximum daily streamflows at the gauging sites of Godavari upper, Godavari middle, Pranahitha, Indravathi and Godavari lower sub-basins. The daily streamflow data at Chass, Ashwi and Pachegaon of Godavari upper, Manjalegaon, Dhalegaon, Zari, GR Bridge, Purna and Yelli of Godavari middle, Gandlapet, Mancherial, Somanpally and Perur of Pranahitha, Pathagudem, Chindnar, Sonarpal, Jagdalpur and Nowrangpur of Indravathi, and, Sardaput, Injaram, Konta, Koida and Polavaram of Godavari lower sub-basins for the period varying between 1965–2011, collected from Central Water Commission (CWC), India were used in the analysis. Statistics of annual maximum daily streamflow series during the study period at the gauging sites of sub-basins indicated moderately variedand positively skewed streamflows, and flows with sharp peaks at the upstream gauging sites. Probabilistic analysis of streamflows showed that lognormal or gamma distribution with conventional moments fitted the maximum daily streamflow data at the gauging sites of Godavari sub-basins.Among 2-parameter distributions with L-moments,GPA2 followed by GAM2/LN2 fitted annual maximum daily streamflow data at most of the gauging sites.At the downstream-most gauging sites of Pranahitha, Indravathi and Godavari lower sub-basins, the data followed W2 probability distribution. Among 3-parameter distributions with L-moments, GPA3 at seven gauging sites, W3 and P3 at five gauging sites each, GLOG at four gauging sites and GEV at two gauging sites fitted the data. Based on the performance evaluation, 2 – parameter distributions using L-moments at the upstream, 3 – parameter distributions at the middle and probability distributions using conventional moments at the downstreamgauging sites performed better in the Godavari upper and middle sub-basins. Probability distributions based on conventional moments/ 3-parameter distributions using L-momentsfitted the annual maximum daily streamflow data at the gauging sites in the Pranahitha, Indravathi and Godavari lower sub-basins satisfactorily.

An Alternative One-Parameter Distribution for Bounded Data Modeling Generated from the Lambert Transformation

The beta and Kumaraswamy distributions are two of the most widely used distributions for modeling bounded data. When the histogram of a certain dataset exhibits increasing or decreasing behavior, one-parameter distributions such as the power, Marshall–Olkin extended uniform and skew-uniform distributions become viable alternatives. In this article, we propose a new one-parameter distribution for modeling bounded data, the Lambert-uniform distribution. The proposal can be considered as a natural alternative to well known one-parameter distributions in the statistical literature and, in certain scenarios, a viable alternative even for the two-parameter beta and Kumaraswamy distributions. We show that the density function of the proposal tends to positive finite values at the ends of the support, a behavior that favors good performance in certain scenarios. The raw moments are derived from the moment-generating function and used to describe the skewness and kurtosis behavior. The quantile function is expressed in closed form in terms of the Lambert W function, which allows reparameterizing the distribution such that the involved parameter represents the qth quantile. Thus, for the analysis of a bounded range variable, for which a set of covariates is available, we propose a regression model that relates the qth quantile of the response to a linear predictor through a link function. The parameter estimation is carried out using the maximum likelihood method and the behavior of the estimators is evaluated through simulation experiments. Finally, three application examples are considered in order to illustrate the usefulness of the proposal.

Homogeneous material based acoustic concentrators and rotators with linear coordinate transformation

With the ability to focus and rotate the acoustic field in a given region while keeping the outside region unchanged, the acoustic concentrator and rotator has been developed for the versatile manipulations of acoustic wave. In this letter, we report the design of acoustic concentrator and rotator facilitated by linear coordinate transformation. Compared with the previous ones that have inhomogeneous parameter distributions, the designed devices are composed of several parts with homogeneous parameters, which can be achieved with the help of few homogeneous layered structures. Simulations are also performed to verify the functions of the designed device. The proposed acoustic concentrators and rotators would be useful in numerous applications such as acoustic sensing and communication.