Browsing by Subject "Forecast"
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Item Evaluating hydrodynamic uncertainty in oil spill modeling(2013-05) Hou, Xianlong; Hodges, Ben R.A new method is presented to provide automatic sequencing of multiple hydrodynamic models and automated analysis of model forecast uncertainty. A Hydrodynamic and oil spill model Python (HyosPy) wrapper was developed to run the hydrodynamic model, link with the oil spill, and visualize results. The HyosPy wrapper completes the following steps automatically: (1) downloads wind and tide data (nowcast, forecast and historical); (2) converts data to hydrodynamic model input; (3) initializes a sequence of hydrodynamic models starting at pre-defined intervals on a multi-processor workstation. Each model starts from the latest observed data, so that the multiple models provide a range of forecast hydrodynamics with different initial and boundary conditions reflecting different forecast horizons. As a simple testbed for integration strategies and visualization on Google Earth, a Runge-Kutta 4th order (RK4) particle transport tracer routine is developed for oil spill transport. The model forecast uncertainty is estimated by the difference between forecasts in the sequenced model runs and quantified by using statistics measurements. The HyosPy integrated system with wind and tide force is demonstrated by introducing an imaginary oil spill in Corpus Christi Bay. The results show that challenges in operational oil spill modeling can be met by leveraging existing models and web-visualization methods to provide tools for emergency managers.Item Forecast verification: A dispersion modeling perspective(2008-05) Rogers-Van Nice, Rachel G.; Basu, Sukanta; Mulligan, Kevin; Schroeder, John L.The Environmental Protection Agency currently uses AERMOD, an air quality dispersion model to aid in the forecasting of transport and dispersion of air pollution for the U.S. Typically, NWS-ASOS observations (post-processed by EPA-AERMET model) are used as input to the AERMOD model. This traditional framework of running a dispersion model based on point observations is quite problematic from a variety of theoretical standpoints (e.g., lack of representativeness of meteorological data). An alternative viable framework would be to use prognostic meteorological models in conjunction with AERMOD. Indeed, contemporary research shows that the use of prognostic models as a substitute for NWS-ASOS observations alleviates some of the longstanding dispersion modeling problems, but at the same time creates new concerns. I will elaborate on several questions that need to be adequately addressed before prognostic models can be reliably utilized in operational dispersion applications. Most of these questions are rooted in prognostic models’ (in) ability to accurately represent the boundary layer variables of interest to the dispersion modeling community (e.g., wind speed, wind direction, temperature). I will compare the potential of a new generation prognostic meteorological model called the Weather Research and Forecasting (WRF) model in capturing wind speed variable versus data from the West Texas Mesonet by statistical analysis for verification. One year of ARW WRF output is analyzed. The WRF is a 36/12 km two-way nested run using the YSU PBL scheme. With use of innovative strategies for verification of complex spatio-temporal forecast fields and novel verification measures will make this study distinct.Item Probabilistic Performance Forecasting for Unconventional Reservoirs With Stretched-Exponential Model(2011-08-08) Can, BunyaminReserves estimation in an unconventional-reservoir setting is a daunting task because of geologic uncertainty and complex flow patterns evolving in a long-stimulated horizontal well, among other variables. To tackle this complex problem, we present a reserves-evaluation workflow that couples the traditional decline-curve analysis with a probabilistic forecasting frame. The stretched-exponential production decline model (SEPD) underpins the production behavior. Our recovery appraisal workflow has two different applications: forecasting probabilistic future performance of wells that have production history; and forecasting production from new wells without production data. For the new field case, numerical model runs are made in accord with the statistical design of experiments for a range of design variables pertinent to the field of interest. In contrast, for the producing wells the early-time data often need adjustments owing to restimulation, installation of artificial-lift, etc. to focus on the decline trend. Thereafter, production data of either new or existing wells are grouped in accord with initial rates to obtain common SEPD parameters for similar wells. After determining the distribution of model parameters using well grouping, the methodology establishes a probabilistic forecast for individual wells. We present a probabilistic performance forecasting methodology in unconventional reservoirs for wells with and without production history. Unlike other probabilistic forecasting tools, grouping wells with similar production character allows estimation of self-consistent SEPD parameters and alleviates the burden of having to define uncertainties associated with reservoir and well-completion parameters.Item Production Forecast, Analysis and Simulation of Eagle Ford Shale Oil(2014-12-02) Alotaibi, Basel Z S Z JIn previous works and published literature, production forecast and production decline of unconventional reservoirs were done on a single-well basis. The main objective of previous works was to estimate the ultimate recovery of wells or to forecast the decline of wells in order to estimate how many years a well could produce and what the abandonment rate was. Other studies targeted production data analysis to evaluate the completion (hydraulic fracturing) of shale wells. The purpose of this research is to generate field-wide production forecast of the Eagle Ford Shale (EFS). This study considered oil production of the EFS only. More than 6 thousand oil wells were put online in the EFS basin between 2008 and December 2013. The method started by generating type curves of producing wells to understand their performance. Based on the type curves, a program was prepared to forecast the oil production of EFS based on different drilling schedules; drilling requirements can be calculated based on the desired production rate. To complement the research, analysis of daily production data from the basin was performed. Moreover, single-well simulations were done to compare results with the analyzed data. Findings of this study depended on the proposed drilling and developing scenario of EFS. The field showed potential of producing high oil production rate for a long period of time. The three presented forecasted cases gave and indications of the expected field-wide rate that can be witnessed in the near future in EFS. The method generated by this study is useful for predicting the performance of various unconventional reservoirs for both oil and gas. It can be used as a quick-look tool that can help if numerical reservoir simulations of the whole basin are not yet prepared. In conclusion, this tool can be used to prepare an optimized drilling schedule to reach the required rate of the whole basin.Item Quantification of production recovery using probabilistic approach and semi-analytical model for unconventional oil reservoirs(2015-12) Choi, Bong Joon; Srinivasan, Sanjay; Sepehrnoori, Kamy, 1951-Decline curve analysis is widely applied for production forecasting in oil & gas industry. However, many models do not work for super-tight, unconventional wells with dominant fracture flows. Some novel decline models have been introduced for unconventional plays, but the transition time between the transient and pseudo-steady flow period is difficult to model with such pure empirical relations. Consequently, the decline projections are often inaccurate and furthermore, they are difficult to quantify the uncertainty associated with the predictions. To address these issues, a combined probabilistic approach is proposed that uses a dual-porosity semi-analytical decline model within an extended bootstrap framework in order to provide estimates for the P10, P50 and P90 production profiles. The probabilistic method employed in this research is a data-generative approach that employs modified bootstrap method to generate multiple decline model projections. The semi-analytical model is an approximate decline model that optimizes parameters describing flow in matrix-fracture systems using the observed production profile. In the proposed method, probabilistic approach and semi-analytical decline model are combined. The modified approach is compared to the performances developed with Arps’ hyperbolic model. Both models are fitted by optimizing respective parameters and 50 synthetic data sets are used to draw confidence interval projections. The probabilistic approach is extended by proposing alternate blocking techniques – variance of the mean and analysis of the variance (ANOVA), in place of a scheme based on the autocorrelation exhibited by the decline data, originally implemented by other researchers. The cumulative production and forecast period production errors are calculated for these alternative schemes. For all proposed applications, two unconventional, horizontal oil wells are used to test the results. Both these wells exhibit sharp decline in production rate in the first few months that is related to fracture flow regimes. The results show that the proposed application of semi-analytical model with probabilistic approach significantly improved the projections. The implementation of alternate blocking techniques also show improvement in confidence interval projections, The resultant uncertainty distributions are more accurate and precise than those obtained using the autocorrelation based schemes. The combined results show that ANOVA blocking technique outperformed the other two techniques.Item The effect of multinationality on management earnings forecasts(Texas A&M University, 2005-08-29) Runyan, Bruce WayneThis study examines the relationship between a firm??s degree of multinationality and its managers?? earnings forecasts. Firms with a high degree of multinationality are subject to greater uncertainty regarding earnings forecasts due to the additional risk resulting from the more complex multinational environment. Prior research demonstrates that firms that fail to meet or beat market expectations experience disproportionate market losses at earnings announcement dates. The complexities and greater uncertainty resulting from higher levels of multinationality are expected to be negatively associated with management earnings forecast precision, accuracy, and bias (downward versus upward). Results of the study are mixed. Regarding forecast precision, two measures of multinationality (foreign sales / total sales and the number of geographic segments) are significantly negatively related to management earnings forecast precision. This was the expected relationship. Regarding forecast accuracy, contrary to expectations, forecast accuracy is positively related to multinationality, with regard to the number of geographic segments a firm discloses. Regarding forecast bias, unexpectedly, two measures of multinationality (foreign sales / total sales and number of countries withforeign subsidiaries) are significantly positively related to more optimistic management earnings forecasts.Item The Incremental Benefits of the Nearest Neighbor Forecast of U.S. Energy Commodity Prices(2012-02-14) Kudoyan, OlgaThis thesis compares the simple Autoregressive (AR) model against the k- Nearest Neighbor (k-NN) model to make a point forecast of five energy commodity prices. Those commodities are natural gas, heating oil, gasoline, ethanol, and crude oil. The data for the commodities are monthly and, for each commodity, two-thirds of the data are used for an in-sample forecast, and the remaining one-third of the data are used to perform an out-of-sample forecast. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are used to compare the two forecasts. The results showed that one method is superior by one measure but inferior by another. Although the differences of the two models are minimal, it is up to a decision maker as to which model to choose. The Diebold-Mariano (DM) test was performed to test the relative accuracy of the models. For all five commodities, the results failed to reject the null hypothesis indicating that both models are equally accurate.Item Using the Hubbert curve to forecast oil production trends worldwide(Texas A&M University, 2007-09-17) Almulla, Jassim M.Crude oil is by far the most important commodity to humans after water and food. Having a continuous and affordable supply of oil is considered a basic human right in this day and age. That is the main reason oil companies are in a constant search of cost effective ways and technologies that allow for an improved oil recovery rate. This would improve profitability as well. What almost everyone knows and dreads at the same time is that oil is an exhaustible resource. This means that as more oil is being produced every day, the amount of oil that remains to be produced shrinks even more. With almost all big oil fields worldwide having already been discovered, the challenge of finding new reserves grows harder and harder. A question that has always been asked is ??????when are we going to run out of oil??????? Given the available technologies and techniques, no one could give an exact answer and if someone does, he/she would not be 100% sure of that answer. This study tries to approximate future oil production rates to the year 2050 using the Hubbert model. There are different models or tools to estimate future oil production rates, but the reason that the Hubbert model was chosen for this study is its simplicity and data availability. As any forecast, this study depends heavily on past trends but also factors in the current conditions. It is safe to say that this forecast (study) is as any other forecast, in which it will probably not mirror exactly what will happen in the future. Still, forecasts have to be done, especially for such an important commodity. This study predicts that the total oil to be recovered is 4.1 trillion barrels. It also shows that most major oil-producing countries are either passed or about to pass their peaks.