Well production forecast in Volve field: Application of rigorous machine learning techniques and metaheuristic algorithm

Ng, Cuthbert Shang Wui; Ghahfarokhi, Ashkan Jahanbani; Amar, Menad Nait

doi:10.1016/j.petrol.2021.109468

Cited by 33 publications

(15 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data source in this study comes from the daily production data of a single well belonging to the Volve Field, an offshore field located in Norway. 27 The data is derived from the multiphase flow period of a production well (oil, gas, water). The shut-in periods are removed since they are manually controlled and unpracticable for WOR and GOR computation.…”

Section: Case Studymentioning

confidence: 99%

See 1 more Smart Citation

Hyperparameter Tuning of Artificial Neural Networks for Well Production Estimation Considering the Uncertainty in Initialized Parameters

et al. 2022

View full text Add to dashboard Cite

A well production rate is an essential parameter in oil and gas field development. Traditional models have limitations for the well production rate estimation, e.g., numerical simulations are computation-expensive, and empirical models are based on oversimplified assumptions. An artificial neural network (ANN) is an artificial intelligence method commonly used in regression problems. This work aims to apply an ANN model to estimate the oil production rate (OPR), water oil ratio (WOR), and gas oil ratio (GOR). Specifically, data analysis was first performed to select the appropriate well operation parameters for OPR, WOR, and GOR. Different ANN hyperparameters (network, training function, and transfer function) were then evaluated to determine the optimal ANN setting. Transfer function groups were further analyzed to determine the best combination of transfer functions in the hidden layers. In addition, this study adopted the relative root mean square error with the statistical parameters from a stochastic point of view to select the optimal transfer functions. The optimal ANN model’s average relative root mean square error reached 6.8% for OPR, 18.0% for WOR, and 1.98% for GOR, which indicated the effectiveness of the optimized ANN model for well production estimation. Furthermore, comparison with the empirical model and the inputs effect through a Monte Carlo simulation illustrated the strength and limitation of the ANN model.

show abstract

Section: Case Studymentioning

confidence: 99%

“…The data source in this study comes from the daily production data of a single well belonging to the Volve Field, an offshore field located in Norway . The data is derived from the multiphase flow period of a production well (oil, gas, water).…”

Section: Case Studymentioning

confidence: 99%

Hyperparameter Tuning of Artificial Neural Networks for Well Production Estimation Considering the Uncertainty in Initialized Parameters

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Forecasting oil production is an urgent means for petroleum engineers and oil companies to form effective links between oil reservoir developments and their profitability [ 1 , 2 , 3 ]. To achieve this ultimate goal, two elements need to be accomplished.…”

Section: Introductionmentioning

confidence: 99%

“…The history-matching mechanism is challenging and time-consuming. Hence, integrating geological models and dynamic models is the main step to ensure that we can perform oil-production forecasting [ 3 , 4 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…They demonstrated their model’s ability to capture the long-term dependencies of time-series data instead of using huge amounts of memory. The authors in [ 3 ] employed various machine learning techniques to develop models that forecast the oil well production in the Volve field. They revealed that the developed models have a high-prediction-output performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting

Al-qaness

Ewees

Abualigah

et al. 2022

Entropy

View full text Add to dashboard Cite

The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is an ANNs that has showed promising performance in time-series prediction. The DNR has the capability to deal with the nonlinear characteristics of historical data for time-series forecasting applications. However, it faces certain limitations in training and configuring its parameters. To this end, we utilized the power of metaheuristic optimization algorithms to boost the training process and optimize its parameters. A comprehensive evaluation is presented in this study with six MH optimization algorithms used for this purpose: whale optimization algorithm (WOA), particle swarm optimization algorithm (PSO), genetic algorithm (GA), sine–cosine algorithm (SCA), differential evolution (DE), and harmony search algorithm (HS). We used oil-production datasets for historical records of crude oil production from seven real-world oilfields (from Tahe oilfields, in China), provided by a local partner. Extensive evaluation experiments were carried out using several performance measures to study the validity of the DNR with MH optimization methods in time-series applications. The findings of this study have confirmed the applicability of MH with DNR. The applications of MH methods improved the performance of the original DNR. We also concluded that the PSO and WOA achieved the best performance compared with other methods.

show abstract

Applications of Data-Driven Techniques in Reservoir Simulation and Management

Jahanbani Ghahfarokhi

2024

Artificial Intelligent Approaches in Petroleum Geosciences

View full text Add to dashboard Cite

Well production forecast in Volve field: Application of rigorous machine learning techniques and metaheuristic algorithm

Cited by 33 publications

References 42 publications

Hyperparameter Tuning of Artificial Neural Networks for Well Production Estimation Considering the Uncertainty in Initialized Parameters

Hyperparameter Tuning of Artificial Neural Networks for Well Production Estimation Considering the Uncertainty in Initialized Parameters

Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting

Applications of Data-Driven Techniques in Reservoir Simulation and Management

Contact Info

Product

Resources

About