Abstract
Oil production forecasting is an important task to manage petroleum reservoirs operations. In this study, a developed time series forecasting model is proposed for oil production using a new improved version of the adaptive neuro-fuzzy inference system (ANFIS). This model is improved by using an optimization algorithm, the slime mould algorithm (SMA). The SMA is a new algorithm that is applied for solving different optimization tasks. However, its search mechanism suffers from some limitations, for example, trapping at local optima. Thus, we modify the SMA using an intelligence search technique called opposition-based learning (OLB). The developed model, ANFIS-SMAOLB, is evaluated with different real-world oil production data collected from two oilfields in two different countries, Masila oilfield (Yemen) and Tahe oilfield (China). Furthermore, the evaluation of this model is considered with extensive comparisons to several methods, using several evaluation measures. The outcomes assessed the high ability of the developed ANFIS-SMAOLB as an efficient time series forecasting model that showed significant performance.
Highlights
Forecasting oil production is a significant step for controlling the management of the cost-effect and monitoring the operation of petroleum reservoirs
The proposed model depends on improving the performance of adaptive neuro-fuzzy inference system (ANFIS) based on enhanced slime mould algorithm (SMA) according to the value opposition-based learning (OBL)
We implemented several experiments considering several evaluation metrics and statistical tests to evaluate the performance of the developed ANFIS-SMAOLB
Summary
Forecasting oil production is a significant step for controlling the management of the cost-effect and monitoring the operation of petroleum reservoirs. In (Alalimi et al 2021), a modified Random Vector Functional Link network was proposed for time series prediction This model was applied for oil production in Tahe oilfield, China. Al-Shabandar et al (2021) presented a new model for prediction oil production using a deep-gated RNN that comprises several hidden layers, in which each one has a set of nodes This model had been evaluated with long-term time-series data. The first step in the developed model, named SMAOLBANFIS, is to split the oil production dataset into training and testing sets, using the training set during the learning stage In this stage, the developed SMAOLB-ANFIS constructs a population X, which has a set of N solutions; each of one refers to one configuration from the parameters of ANFIS.
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