Sophisticated ROP Prediction Technologies Based on Neural Network Delivers Accurate Drill Time Results

Abstract

Accurately predicting the rate of penetration (ROP) is a prominent factor affecting economic and engineering decisions during well planning. However, ROP prediction based on simple algorithms applied to offset wells has historically yielded mixed results. To improve ROP predicting capabilities, the provider is applying an artificial neural network (ANN) to analyze offset drilling data. The system has significantly improved the ability to accurately predict drilling performance, despite expected changes in lithology, hole size, bit type and mud properties. The flexibility of the software package allows engineers to analyze a wide range of information and deliver high-quality ROP predictions based on previous experience and data from offset wells. The process includes detail scrutiny of the offset and training of the ANN system until the neural network is validated. The simulation is then run to solve expected ROP, using any changed drilling conditions as input. In a recent case, an operator required a well that would have to be drilled deeper than offsets and with different borehole sizes. By applying the neural network capabilities, engineers were able to deliver analytical ROP predictions for the planned well, including quality result for different lithologies with a wide range of rock strength values. This paper will focus on the new ANN technologies being utilized for predicting drilling performance. It will also include a case study that documents its successful application.

Artificial neural network models to predict lost circulation in natural and induced fractures

“…This is done to ensure the model's robustness at generalizing to new data. Training data are used in training, verification data are Arehart [18] Drill bit diagnosis Used ANNs to find the state of wear of drill bit during drilling Dashevskiy et al [19] Real-time drilling dynamic Modeling the dynamic behavior of drilling system Bilgesu et al [20] Drill bit selection Used ANNs to select the "best" bit based on some inputs Ozbayoglu et al [21] Bed height for horizontal wells Used ANNs to predict bed heights in horizontal or highly inclined wellbores Vassallo et al [22] Bit bounce detection Used ANNs to detect bit bounce that can be used as a proactive approach to prevent bit whirl and stick-slip Fruhwirth et al [23],Wang and Salehi [24] Drilling hydraulics optimization and prediction Used ANNs to optimize and predict drilling hydraulics with a practical example Moran et al [25],Al-AbdulJabbar et al [26] Rate of penetration (ROP) prediction Used ANNs to predict ROP so that the drill time can be estimated better Gidh et al [27] Bit wear prediction Used ANNs to predict/ manage bit wear to improve ROP Lind & Kabirova [28] Drilling troubles prediction Used ANNs to forecast problems during the drilling process Okpo et al [29] Wellbore instability Wellbore stability prediction Ahmadi et al [30] Prediction of mud weight at wellbore conditions…”

New Artificial Neural Networks Model for Predicting Rate of Penetration in Deep Shale Formation

“…Moran [17] used ANN to study legacy-drilling data and improved the prediction of ROP. He used six input parameters including RPM, WOB, MW, rock strength, abrasion and type of the rock.…”

Enhancing Shear Velocity Log Prediction in Ahnet Field, Algeria, Through Well Logs and Machine Learning Techniques

“…ML algorithms have better generalization ability and can discover and extract hidden trends and relationships from huge datasets that were previously impossible to explore manually (Suleymanov et al, 2021). Machine learning techniques have been used successfully for a wide range of tasks in the oil and gas industry, from exploration and geophysics applications to production, such as seismic data processing (Karrenbach et al, 2000), ROP prediction (Moran et al, 2010), UCS prediction (Chellal et al 2023), drilling optimization (Ouadi et al, 2023), water saturation prediction , mineralogy prediction (Laalam et al, 2022), stress-dependent porosity and permeability prediction (Ouadi et al, 2022), enhanced oil recovery applications (Chemmakh et al, 2021), and completion design (Laoufi et al, 2022), Therefore, our study seeks to investigate the integration of machine learning and well logs for shear velocity prediction in the Ahnet field, Algeria. The Ahnet field is a naturally fractured reservoir (Irofti et al, 2022), located in a region of significant hydrocarbon potential, and presents an ideal testing ground to explore the practicality and efficiency of this predictive approach.…”