5. Conclusion

same process. The density and amount of data used for Well-B as can be seen in Table 3, is

The application of domain knowledge and in particular, the utilization of specific energy as a concept in selecting the controllable drilling parameters used in the prediction of ROP has proven valuable with all the AI models showing accuracy within acceptable range. A depth plot of actual ROP against the predicted ROP from all the AI models is presented in Figure 6. As can be observed, the qualitative difference is quite elusive showing that the four AI models

In summary, the LS-SVR produces the best ROP model for the two dataset in term of accuracy, while it requires considerable amount of testing time of the four AI techniques compared. Therefore, it is more suitable for situations where accuracy is most desirable. Whereas, ELM and ANN requires the shortest testing execution time and are less accurate, they are more

evidently responsible for the extra time it takes for testing the model.

Figure 5. Testing time for each of the algorithms tested with the two datasets.

Figure 4. CC-RMSE plot showing testing results for dataset 1 and 2 for wells A and B, respectively.

are good predictors with reasonable accuracy.

124 Drilling

AI techniques have increasingly proved to be of immense value in the oil and gas industry where it has been employed by different segments of the industry. Traditional methods has not been able to manage such huge impacts in such a short time as AI methods because of its ability to decipher hidden codes and complex relationships within the enormous data collected daily during drilling operations. However, application of the right domain expert knowledge has shown improved performance in the deployment of AI techniques. This technique and its application leads to time and cost saving, minimized risk, improved efficiency and solutions many optimization problems. The ability of the technique to retrain itself with life data within a shorter time has made it a major founding block for drilling automation.

This paper presents an improved methodology of predicting ROP with real-time drilling optimization in mind. Recent studies in the use of AI in the prediction of ROP shows some inconsistency in the selection of input variables. The parameters used in this study are the must haves and easily accessible parameters which can mostly be adjusted while drilling and are therefore controllable. The utilization of HMSE-ROP model has also enhanced the performance of the models as a result of selecting few variables with established relationship to ROP even though nonlinear. All the methods used provided good degree of accuracy, and therefore presented the engineers with options to use whichever algorithm is suitable for their scenarios. It is therefore recommended that the HMSE variables should always be included in the data attributes in the prediction of ROP as they are good predictors.

PDM positive displacement motor

SFLA shuffled frog leaping algorithm

SLFN single-hidden layer feedforward neural

RMSE root mean square error ROP rate of penetration, ft/h

RPM rotation per minute SDL surface data logging

SPM strokes per minutes SPP stand pipe pressure

TDS top drive system

TVD true vertical depth

WOB weight on bit, lbs

WDM warren drilling model

W function of WOB and db

Cfd formation drillability parameter

η dimensionless energy reduction factor depending on bit diameter

\*Address all correspondence to: saomogbolahan2@live.utm.my

\*, Ahmed Adeniran<sup>2</sup> and Ariffin Samsuri<sup>1</sup>

1 Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, Johor Bahru,

2 Department of Systems Engineering, King Fahd University of Petroleum and Minerals,

t time, h

TG total gas TRQ torque

Author details

Johor, Malaysia

Omogbolahan Ahmed1

Dhahran, Saudi Arabia

SVR support vector regression

SE specific energy

Q mud flow-in rate in gallons per minute

Rate of Penetration Prediction Utilizing Hydromechanical Specific Energy

http://dx.doi.org/10.5772/intechopen.76903

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## Nomenclature


