Oil production forecasting is one of the essential processes for organizations and governments to make necessary economic plans. This paper proposes a novel hybrid intelligence time series model to forecast oil production from two different oil fields in China and Yemen. This model is a modified ANFIS (Adaptive Neuro-Fuzzy Inference System), which is developed by applying a new optimization algorithm called the Aquila Optimizer (AO). The AO is a recently proposed optimization algorithm that was inspired by the behavior of Aquila in nature. The developed model, called AO-ANFIS, was evaluated using real-world datasets provided by local partners. In addition, extensive comparisons to the traditional ANFIS model and several modified ANFIS models using different optimization algorithms. Numeric results and statistics have confirmed the superiority of the AO-ANFIS over traditional ANFIS and several modified models. Additionally, the results reveal that AO is significantly improved ANFIS prediction accuracy. Thus, AO-ANFIS can be considered as an efficient time series tool.

/pdf/optimized-anfis-model-using-aquila-optimizer-for-oil-574zomai9e.pdf

Optimized ANFIS Model Using Aquila Optimizer for Oil Production Forecasting

http://diposit.ub.edu/dspace/bitstream/2445/162486/1/687154.pdf

Impact of pore structure and fractal characteristics on the sealing capacity of Ordovician carbonate cap rock in the Tarim Basin, China

The application of N2 huff and puff for IOR in fracture-vuggy carbonate reservoir

Lithofacies and reservoir characteristics of saline lacustrine fine-grained sedimentary rocks in the northern Dongpu Sag, Bohai Bay Basin: Implications for shale oil exploration

In China, Tahe Triassic oil field block 9 reservoir was discovered in 2002 by drilling wells S95 and S100. The distribution of the reservoir sand body is not clear. Therefore, it is necessary to study and to predict oil production from this oil field. In this study, we propose an improved Random Vector Functional Link (RVFL) network to predict oil production from Tahe oil field in China. The Spherical Search Optimizer (SSO) is applied to optimize the RVFL and to enhance its performance, where SSO works as a local search method that improved the parameters of the RVFL. We used a historical dataset of this oil field from 2002 to 2014 collected by a local partner. Our proposed model, called SSO-RVFL, has been evaluated with extensive comparisons to several optimization methods. The outcomes showed that, SSO-RVFL achieved accurate predictions and the SSO outperformed several optimization methods.

/pdf/optimized-random-vector-functional-link-network-to-predict-2xmz8uhd36.pdf

Optimized Random Vector Functional Link network to predict oil production from Tahe oil field in China

Identification and characteristic analysis of carbonate cap rock: A case study from the Lower-Middle Ordovician Yingshan Formation in Tahe oilfield, Tarim Basin, China

Understanding and predicting the main controls on the sealing capacity of carbonate cap rocks is of great significance for ultradeep carbonate reservoir exploration and production. This study focuses on revealing the pore networks and sealing capacity of the Ordovician carbonate cap rocks in the Tarim Basin by analyzing samples from outcrop analogues using optical and scanning electron microscopy and a combination of mercury intrusion capillary pressure and nitrogen gas adsorption. Three classes of cap rocks are defined here according to their pore-throat structure, fractal dimension, and sealing capacity. These carbonate cap rocks are dominated by limestones and dolomitic limestones. Four pore types are identified: microfractures, intragranular, intercrystalline, and intracrystalline. Six pore structure types show multiscale variability from macropores to micropores. The pore structures present multiple fractal behaviors, with fractal dimensions showing an increasing trend as the pore diameter decreases. The cover coefficient, a parameter that allows characterization of the caprock sealing performance, shows an increasing trend along with increasing the fractal dimension of pore structure. The average cover coefficients of six pore structure types not only show good correlations (either exponential or linear) with certain fractal dimensions, but they also demonstrate a strong positive correlation with the average fractal dimension. These results suggest that the sealing capacity of the studied rocks increases with increasing fractal dimension. The sealing performance of cap rocks significantly decreases with an increasing amount of macropores. This work provides a relevant case study for further understanding of pore structures and sealing capacity of carbonate cap rocks.

/pdf/fractal-characteristics-of-pore-networks-and-sealing-10br5n90o9.pdf

Fractal characteristics of pore networks and sealing capacity of Ordovician carbonate cap rocks: A case study based on outcrop analogues from the Tarim Basin, China

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Identification and characteristic analysis of carbonate cap rock: A case study from the Lower-Middle Ordovician Yingshan Formation in Tahe oilfield, Tarim Basin, China

Fractal characteristics of pore networks and sealing capacity of Ordovician carbonate cap rocks: A case study based on outcrop analogues from the Tarim Basin, China