Global Petroleum reserves are currently getting depleted. Most of the newly discovered oil and gas fields are found in
unconventional reserves. Hence there has arisen a need to drill deeper wells in offshore locations and in unconventional
reservoirs. The depth and difficulty of drilling terrains has led to drilling operations incurring higher cost due to drilling
time. Rate of Penetration is dependent on the several parameters such as: rotary speed(N), Weight-On-Bit, bit state,
formation strength, formation abrasiveness, bit diameter, mud flowrate, bit tooth wear, bit hydraulics e.t.c. Given this
complex non-linear relationship between Rate of Penetration and these variables, it is extremely difficult to develop a
complete mathematical model to accurately predict ROP from these parameters. In this study, two types of models were
developed; a predictive model built with artificial neural networks for determining the rate of penetration from various
drilling parameters and an optimization model based on normalized rate of penetration to provide optimized rate of
penetration values. The Normalized Rate of Penetration (NROP) more accurately identifies the formation characteristics
by showing what the rate should be if the parameters are held constant. Lithology changes and pressure transition zones
are more easily identified using NROP. Efficient use of Normalized Penetration Rate (NROP) reduces drilling expenses
by: Reducing the number of logging trips, minimizing trouble time through detection of pressure transition zones,
encouraging near balanced drilling to achieve faster penetration rate.
Ojuolape, T. (2021). Drilling Cost Optimization for Extended Reach Deep Wells Using Artificial Neural Networks. Afribary. Retrieved from https://afribary.com/works/drilling-cost-optimization-for-extended-reach-deep-wells-using-artificial
Ojuolape, Toheeb "Drilling Cost Optimization for Extended Reach Deep Wells Using Artificial Neural Networks" Afribary. Afribary, 25 Oct. 2021, https://afribary.com/works/drilling-cost-optimization-for-extended-reach-deep-wells-using-artificial. Accessed 23 May. 2022.
Ojuolape, Toheeb . "Drilling Cost Optimization for Extended Reach Deep Wells Using Artificial Neural Networks". Afribary, Afribary, 25 Oct. 2021. Web. 23 May. 2022. < https://afribary.com/works/drilling-cost-optimization-for-extended-reach-deep-wells-using-artificial >.
Ojuolape, Toheeb . "Drilling Cost Optimization for Extended Reach Deep Wells Using Artificial Neural Networks" Afribary (2021). Accessed May 23, 2022. https://afribary.com/works/drilling-cost-optimization-for-extended-reach-deep-wells-using-artificial