Application of hybrid artificial neural networks for predicting rate of penetration (ROP): A case study from Marun oil field

Ashrafi, Seyed Babak; Anemangely, Mohammad; Sabah, Mohammad; Ameri, Mohammad

doi:10.1016/j.petrol.2018.12.013

Cited by 118 publications

(38 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies on investigating the effect of other input parameters can be conducted for better results. [ 38 ]…”

Section: Suggestions and Discussionmentioning

confidence: 99%

“…Ashrafi et al [ 38 ] presented eight hybrid and two simple ANN models that were trained by four different algorithms to predict the ROP (m h −1 ). The performance indicators used in this study are RMSE, R , VAF (%), and performance index (PI).…”

Section: And Da Applications In Upstream Petroleum Industrymentioning

confidence: 99%

“…Studies on investigating the effect of other input parameters can be conducted for better results. [38] Studies on investigating the possibility of predicting failures and accidental events can be performed. [39] More investigations can be performed to optimize the drilling parameters using other methods such as random K-nearest neighbor and comparing the performance.…”

Section: Exploration and Drilling Operationsmentioning

confidence: 99%

See 2 more Smart Citations

Identifying Applications of Machine Learning and Data Analytics Based Approaches for Optimization of Upstream Petroleum Operations

2020

View full text Add to dashboard Cite

In the mid-19th century, the first oil well was drilled, which marked the beginning of the world oil economy. This era embarked on the phase of evolution and development of society in every aspect, with huge dependence on petroleum products, which subsequently led to tremendous growth in terms of technological advancements in the exploration and production activities over time. With the existing petroleum fields approaching toward depletion, the global need for energy resources is increasing simultaneously. This continuously rising demand has led us to move to complex environments for oil and gas exploration and production activities. Moreover, the real-time monitoring of reservoirs and well operations produces enormous data, which require efficient processing and analysis tools for decisionmaking regarding field development and asset management. [1,2] Subsequently, the need for optimum facilities design, [3] transportation [4] and refining [5] activities at reduced cost has also grown. Several attempts [6-9] are also being made to develop unconventional fields commercially based on technoeconomic viability. The implementation of computer-based techniques has proven to be promising toward many challenging problems for various oilfield operations. Several researchers have shown that the machine learning (ML) and data analytics (DA) approaches hold promising solutions toward the operational challenges being encountered at present in the industry and can efficiently help in resolving the issues related to interpretation and analysis of large datasets. [10-15] Nikravesh and Aminzadeh [16] have shown the use of neural networks (NNs) and fuzzy logic for mining petroleum data. Li and Li [17] combined a NN and cluster analysis to put forth a predictive model for identifying complex lithology. Fath et al. [18] proposed a novel approach for bubble point pressure prediction using an NN model with reservoir temperature, solution gas oil ratio (GOR), oil gravity, and gas specific gravity as input attributes. Ahmadi and Bahadori [19] showed the use of a supervised learning algorithm to determine the well placement and conning occurrence in horizontal wells. Maucec et al. [20] performed data mining and ML on well stimulation data for enhancing the prediction capabilities. Gupta et al. [21] applied DA for safeguarding real-time electrical submersible pumping operations. Cadei et al. [22] forecasted operational upsets using advanced analytics in an upstream production system. Dongxiao et al. [23] presented in their research that long short-term memory (LSTM), cascaded LSTM, and a fully connected NN can be utilized for generating synthetic well logs by supplementing the missing logging input data. Ghorbani et al. [24] proposed an artificial Intelligence (AI)based approach to predict the flow rate of oil from an orifice

show abstract

“…Studies on investigating the effect of other input parameters can be conducted for better results. [ 38 ]…”

Section: Suggestions and Discussionmentioning

confidence: 99%

Section: And Da Applications In Upstream Petroleum Industrymentioning

confidence: 99%

Section: Exploration and Drilling Operationsmentioning

confidence: 99%

See 1 more Smart Citation

Identifying Applications of Machine Learning and Data Analytics Based Approaches for Optimization of Upstream Petroleum Operations

2020

View full text Add to dashboard Cite

show abstract

“…In our research, we used an averaging number of 7. The Savitsky-Golay filter is a low-pass filter that provides a method for smoothing time-series data using local leastsquares polynomial approximation smoothing technique [38] . Savitsky-Golay was used to preprocess the data used to predict PM 2.5 in [13] and improved the results drastically.…”

Section: Preprocessing Smoothing Filtersmentioning

confidence: 99%

Ground-level Ozone Prediction Using Machine Learning Techniques: A Case Study in Amman, Jordan

Aljanabi

Shkoukani

Hijjawi

2020

Int. J. Autom. Comput.

View full text Add to dashboard Cite

Air pollution is one of the most serious hazards to humans′ health nowadays, it is an invisible killer that takes many human lives every year. There are many pollutants existing in the atmosphere today, ozone being one of the most threatening pollutants. It can cause serious health damage such as wheezing, asthma, inflammation, and early mortality rates. Although air pollution could be forecasted using chemical and physical models, machine learning techniques showed promising results in this area, especially artificial neural networks. Despite its importance, there has not been any research on predicting ground-level ozone in Jordan. In this paper, we build a model for predicting ozone concentration for the next day in Amman, Jordan using a mixture of meteorological and seasonal variables of the previous day. We compare a multi-layer perceptron neural network (MLP), support vector regression (SVR), decision tree regression (DTR), and extreme gradient boosting (XGBoost) algorithms. We also explore the effect of applying various smoothing filters on the time-series data such as moving average, Holt-Winters smoothing and Savitzky-Golay filters. We find that MLP outperformed the other algorithms and that using Savitzky-Golay improved the results by 50% for coefficient of determination (R 2) and 80% for root mean square error (RMSE) and mean absolute error (MAE). Another point we focus on is the variables required to predict ozone concentration. In order to reduce the time required for prediction, we perform feature selection which greatly reduces the time by 91% as well as shrinking the number of features required for prediction to the previous day values of ozone, humidity, and temperature. The final model scored 98.653% for R 2 , 1.016 ppb for RMSE and 0.800 ppb for MAE.

show abstract

“…ANN has many applications in the petroleum industry as summarized by Al-Bulushi et al [23]. ANN has been applied in different aspects of petroleum engineering such as production forecasting [24,25], PVT (Pressure, volume, temperature) parameter prediction [26], well integrity evaluation [27], drilling fluid properties [28][29][30], reservoir, rock mechanics [31][32][33][34][35], drilling optimization [36][37][38][39][40][41], and permeability determination from well logs [42].…”

Section: Artificial Neural Network and Its Application In Drilling Opmentioning

confidence: 99%

A New Model for Predicting Rate of Penetration Using an Artificial Neural Network

Elkatatny

Al-AbdulJabbar

Abdelgawad

2020

Sensors

View full text Add to dashboard Cite

The drilling rate of penetration (ROP) is defined as the speed of drilling through rock under the bit. ROP is affected by different interconnected factors, which makes it very difficult to infer the mutual effect of each individual parameter. A robust ROP is required to understand the complexity of the drilling process. Therefore, an artificial neural network (ANN) is used to predict ROP and capture the effect of the changes in the drilling parameters. Field data (4525 points) from three vertical onshore wells drilled in the same formation using the same conventional bottom hole assembly were used to train, test, and validate the ANN model. Data from Well A (1528 points) were utilized to train and test the model with a 70/30 data ratio. Data from Well B and Well C were used to test the model. An empirical equation was derived based on the weights and biases of the optimized ANN model and compared with four ROP models using the data set of Well C. The developed ANN model accurately predicted the ROP with a correlation coefficient (R) of 0.94 and an average absolute percentage error (AAPE) of 8.6%. The developed ANN model outperformed four existing models with the lowest AAPE and highest R value.

show abstract

Application of hybrid artificial neural networks for predicting rate of penetration (ROP): A case study from Marun oil field

Cited by 118 publications

References 66 publications

Identifying Applications of Machine Learning and Data Analytics Based Approaches for Optimization of Upstream Petroleum Operations

Identifying Applications of Machine Learning and Data Analytics Based Approaches for Optimization of Upstream Petroleum Operations

Ground-level Ozone Prediction Using Machine Learning Techniques: A Case Study in Amman, Jordan

A New Model for Predicting Rate of Penetration Using an Artificial Neural Network

Contact Info

Product

Resources

About