An Artificial Neural Network for Predicting Rate of Penetration in AL- Khasib Formation â€“ Ahdeb Oil Field

Zirej, Zaher JabbarAttwan Al; Hadi, Hassan Abdul

doi:10.24996/ijs.2020.61.5.14

Cited by 4 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Adaptive neural networks The ANN can detect complex patterns within the databases that the arithmetic formulas cannot find [8], [9], [10], and [11]. In addition, it produces quite accurate forecasts, even for noisy data.…”

Section: Artificial Neural Network (Anns) and Gismentioning

confidence: 99%

Finding the Best Route for Connecting Citizens with Service Centers in Baghdad Based on NN Technology

Ali,

Mohammed,

Mohammed

2024

Iraqi Journal of Science

View full text Add to dashboard Cite

A geographic information system (GIS) is a very effective management and analysis tool. Geographic locations rely on data. The use of artificial neural networks (ANNs) for the interpretation of natural resource data has been shown to be beneficial. Back-propagation neural networks are one of the most widespread and prevalent designs. The combination of geographic information systems with artificial neural networks provides a method for decreasing the cost of landscape change studies by shortening the time required to evaluate data. Numerous designs and kinds of ANNs have been created; the majority of them are PC-based service domains. Using the ArcGIS Network Analyst add-on, you can locate service regions around any network site. A network service area is a region that comprises all accessible roadways (that is, routes that are within defined impedance) (that is, streets that are within specified impedance). In contrast to the Google Maps application, the 5-minute service area for a site on a network comprises all streets that can be accessed within five minutes.

show abstract

Section: Artificial Neural Network (Anns) and Gismentioning

confidence: 99%

Finding the Best Route for Connecting Citizens with Service Centers in Baghdad Based on NN Technology

Ali,

Mohammed,

Mohammed

2024

Iraqi Journal of Science

View full text Add to dashboard Cite

show abstract

“…They found that the two highest significant parameters on drilling rate were RPM and WOB. [16][17][18][19]. In this study, a parameter of aerated drilling fluid, namely foam flow rate (FF) was included as an input variable along with parameters that are generally used for both geothermal and hydrocarbon fields in the ANN model.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Network to Estimate Rate of Penetration for Geothermal Well Drilling in South Sumatera

Fathaddin¹,

Irawan²,

Marhaendrajana³

et al. 2023

IJETAE

View full text Add to dashboard Cite

In order to plan an optimum geothermal well drilling scheme, a proper identification of drilling parameters should be well known. Information of the parameters consists of weight on bit (WOB), true vertical depth (TVD), rate of penetration (ROP), foam flowrate (FF), and rotary speed (N). The valuable information can be provided by the drilled geothermal wells. Correlation of the drilling parameters is then obtained based on the information. The application of Artificial Neural (ANN) Network is needed since the relationships among the parameters are very complex and nonlinear. Moreover, the relationships are not easily known. In this paper, Artificial Neural Network was promoted to estimate penetration rate. Data were obtained from three wells at a field in South Sumatera, Indonesia. Three ANN models were generated. Each model includes different input parameters. Based on the comparison results, the ANN-3 model has the best level of accuracy with the average values of the parameters MAE, MARE, MSE, ARMSE, and the correlation coefficients are 0.8883, 9.54%, 1.1878, 1.0825, and 0.9938 respectively. ANN models can play a role in identifying parameters that affect the characteristics of penetration rate. Keywords—Drilling, WOB, Geothermal, ROP, ANN.

show abstract

Combining Bourgoyne and Young Equations by Bagging Tree Regression to Predict Rate of Penetration in a Southern Iraqi Field, Case Study

Al-Assad,

Hamd-Allah

2024

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Achieving an accurate and optimal rate of penetration (ROP) is critical for a cost-effective and safe drilling operation. While different techniques have been used to achieve this goal, each approach has limitations, prompting researchers to seek solutions. This study’s objective is to conduct the strategy of combining the Bourgoyne and Young (BYM) ROP equations with Bagging Tree regression in a southern Iraqi field. Although BYM equations are commonly used and widespread to estimate drilling rates, they need more specific drilling parameters to capture different ROP complexities. The Bagging Tree algorithm, a random forest variant, addresses these limitations by blending domain knowledge and capturing non-linear relationships. Its ensemble nature also mitigates the impact of outliers. This approach combines physics-based equations with machine learning to enable more accurate ROP predictions in drilling operations. It enhances drilling efficiency, reduces expenses, and improves decision-making in the oil and gas sector. Extensive testing on actual drilling datasets has demonstrated outstanding performance compared to the multiple linear regression (MLR) method. With the increased R2 and zero P-value. positive findings show that this tool can benefit precise future ROP prediction in southern Iraqi oil well drilling.

show abstract

An Artificial Neural Network for Predicting Rate of Penetration in AL- Khasib Formation â€“ Ahdeb Oil Field

Cited by 4 publications

References 12 publications

Finding the Best Route for Connecting Citizens with Service Centers in Baghdad Based on NN Technology

Finding the Best Route for Connecting Citizens with Service Centers in Baghdad Based on NN Technology

Application of Artificial Neural Network to Estimate Rate of Penetration for Geothermal Well Drilling in South Sumatera

Combining Bourgoyne and Young Equations by Bagging Tree Regression to Predict Rate of Penetration in a Southern Iraqi Field, Case Study

Contact Info

Product

Resources

About