Estimating the initial pressure, permeability and skin factor of oil reservoirs using artificial neural networks

Jeirani, Z; Mohebbi, Ali

doi:10.1016/j.petrol.2005.09.002

Cited by 64 publications

(16 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reservoir pressure is one of the key variables that is used to obtain reservoir properties, monitor reservoir condition, and forecast reservoir performance (Athichanagorn et al 2002). In well pressure testing, wellbore pressure and flow rate are monitored to evaluate reservoir characteristics by matching a simplified reservoir model on pressure responses (Blasingame et al 1989;Bourdet and Gringarten 1980;Ghaffarian et al 2014;Horne and Reyner 1995;Jeirani and Mohebbi 2006). Since 1990s, most of wells have been installed with permanent downhole gauges.…”

Section: Introductionmentioning

confidence: 99%

A comparison of methods for denoising of well test pressure data

Moosavi

Qajar

Riazi

2018

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

Pressure transient data from downhole gauges are one of the key parameters in characterizing reservoir properties and forecasting future reservoir performance. Reservoir pressure is usually measured under dynamic changes. The collected data usually contain different levels of noise, particularly due to imperfections in measuring instruments and imperfect calibrations. The latter is due to changes between the laboratory environment and reservoir conditions. To have accurate descriptions of reservoir, it is essential to smooth the pressure data. Most related studies have employed the wavelet transform to reduce noise. However, there appears to be little research addressing the use of other smoothing techniques for pressure transient data. This paper, therefore, evaluates and compares the performance of three types of smoothing and noise removal methods, namely wavelet transform as a widely used filtering technique, regression-based smoothers, and autoregressive smoothing methods to reduce artificial noise added to simulated dual-porosity pressure data. Particularly, noise is more pronounced in pressure derivative, and so denoising of pressure derivative requires more effective tools. The effectiveness of the noise removing methods was compared using mean square error. The results show that the regression-based methods lead to the same or even better reduction in the noise level as compared to the wavelet domain filter, while the employed autoregressive method results in a moderate performance. We also test the performance of various combinations of the different smoothing methods to filter the same noisy data. It is shown that the combined locally weighted scatterplot smooth (LOESS) and autoregressive moving average (ARMA) gives the best smoothing performance for pressure derivative data. Application of the combined LOESS-ARMA to real field data shows promising results.

show abstract

Section: Introductionmentioning

confidence: 99%

A comparison of methods for denoising of well test pressure data

Moosavi

Qajar

Riazi

2018

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

show abstract

“…By analyzing the recorded pressure signal over time which is obtained from well testing operations, reservoir model and its boundary (formation model) can be identified [2]. Moreover some reservoir parameters such as initial reservoir pressure, average conductivity of matrix and fracture, storativity ratio, interporosity flow coefficient, value of reservoir damage can be estimated using these signals [2][3][4]. It should be noted that prior to start the parameter estimation, decision should be made on the formation model.…”

Section: Introductionmentioning

confidence: 99%

Hydrocarbon reservoir model detection from pressure transient data using coupled artificial neural network—Wavelet transform approach

Vaferi

Eslamloueyan

Ghaffarian

2016

Applied Soft Computing

View full text Add to dashboard Cite

“…Neural networks were applied to evaluate bottomhole pressure while underbalanced drilling (Ashena et al, 2011), reservoir pressure, permeability, and skin-factor (Jeirani et al, 2006). Neural networks were applied to evaluate bottomhole pressure while underbalanced drilling (Ashena et al, 2011), reservoir pressure, permeability, and skin-factor (Jeirani et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Application of Data Mining Tools for Analysis and Prediction of Hydraulic Fracturing Efficiency for the BV8 Reservoir of the Povkh Oil Field

Alimkhanov

Samoylova

2014

All Days

View full text Add to dashboard Cite

The technique for selecting candidate wells for hydraulic fracturing and prediction of post-frac well performance using Data Mining tools (neural networks, decision tree algorithm, etc.) is presented. The technique was applied to the BV 8 reservoir in the Povkh Oil Field. The reservoir is characterized by the complex clinoform structure.The objective of the paper is to determine basic criteria for candidate selection and evaluate the effect of different geological and field conditions on the efficiency of hydraulic treatments. To achieve the objective two problems have been solved:1. Development of classification models (efficient/inefficient hydraulic fracturing treatment); 2. Development of regression models (post-frac fluid rate, water cut, oil rate).Machine learning algorithms were used in petroleum industry in the past, but neural networks is the most common technique. Eleven models have been built based on six algorithms. A comparison has been made between the models. A good consistency between predicted values of post-frac fluid rate and water cut and actual results has been revealed.Major geological and field criteria for the efficiency of hydraulic fracturing operations have been identified. The models built can be applied to predict the post-frac performance of a single well as well as to do a quick estimation of the whole well stock. The results presented show significant predictive power of the Data Mining tools for geologically-complex reservoirs. It should be assumed that the usage of the technique for less complex reservoirs will allow us to significantly improve prediction of well performance.

show abstract

Estimating the initial pressure, permeability and skin factor of oil reservoirs using artificial neural networks

Cited by 64 publications

References 5 publications

A comparison of methods for denoising of well test pressure data

A comparison of methods for denoising of well test pressure data

Hydrocarbon reservoir model detection from pressure transient data using coupled artificial neural network—Wavelet transform approach

Application of Data Mining Tools for Analysis and Prediction of Hydraulic Fracturing Efficiency for the BV8 Reservoir of the Povkh Oil Field

Contact Info

Product

Resources

About