| The task of modeling data is difficult when the data of some variables are unavailable either totally or partially during the examined time span. Lacking the data, it is at times impossible to model causal relationships between those variables and the variable to be forecasted. In such a case, a possible solution is to use univariate time series modeling where the historical data of the variable of interest is used to develop a model. In this thesis, a univariate time series approach, using solely the petroleum production and gas flow rate respectively is taken to construct two stand-alone feed-forward neural network forecasting models. Neural network approach was chosen for the tasks due to its ability to handle non-linearity and its freedom from a priori selection of mathematical models. The results of the experiments suggest that one-step-ahead forecasts can be made with reasonably accuracy.; A relatively novel outcome of this thesis is the integration of individual artificial neural networks into a single model that may produce better long-term predictions. Each component network is constructed for making direct forecasts of different time interval ahead. The combination of individual artificial neural networks, called a multiple neural network model, propagates forward in different-length steps in order to make forecasts. Due to the various step-lengths, it is expected that the number of recursion steps is smaller, and hence the accumulative error is lower. |
