Unconfined compressive strength (UCS) prediction in real-time while drilling using artificial intelligence tools

Gowida, Ahmed; Elkatatny, Salaheldin; Gamal, Hany

doi:10.1007/s00521-020-05546-7

Cited by 58 publications

(16 citation statements)

References 30 publications

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“…Based on the literature, ANN has been widely applied in many petroleum-related applications such as the predictions of the mechanical properties of the downhole formations based on the drilling parameters. Examples of these applications are the prediction of Poisson's ratio and unconfined compressive strength (UCS) [21,[35][36][37][38].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

[Retracted] Prediction of the Least Principal Stresses Using Drilling Data: A Machine Learning Application

Gowida

Ibrahim

Elkatatny

et al. 2021

Computational Intelligence and Neuroscience

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The least principal stresses of downhole formations include minimum horizontal stress (σmin) and maximum horizontal stress (σmax). σmin and σmax are substantial parameters that significantly affect the design and optimization of the drilling process. These stresses can be estimated using theoretical equations in addition to some field tests, i.e., leak-off test to include the effect of tectonic stress. This approach is associated with many technical and financial issues. Therefore, the objective of this study is to provide a novel machine learning-based solution to estimate these stresses while drilling. First, new models were developed using artificial neural network (ANN) to directly predict σmin and σmax from the drilling data; which are injection rate (Q), standpipe pressure (SPP), weight on bit (WOB), torque (T), and rate of penetration (ROP). Such data are always available while drilling, and hence, no additional cost is required. Actual data from a Middle Eastern field were collected, statistically analyzed, and fed to the models. First, the models’ predictions showed a significant match with the actual stress values with a correlation coefficient (R-value) exceeding 0.90 and a mean absolute average error (MAPE) of 0.75% as a maximum. Second, new empirical equations were generated based on the developed ANN-based models. The new equations were then validated using another unseen dataset from the same field. The predictions had an R-value of 0.98 and 0.93 in addition to MAPE of 0.36% and 0.96% for σmin and σmax models, respectively. The results demonstrated the outperformance of the developed ANN-based equations to estimate the least principal stresses from the drilling data with high accuracy in a timely and economically effective way.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

[Retracted] Prediction of the Least Principal Stresses Using Drilling Data: A Machine Learning Application

Gowida

Ibrahim

Elkatatny

et al. 2021

Computational Intelligence and Neuroscience

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“…Similarly, Gowida et al used different ML techniques including ANN, ANFIS, and SVM to calculate the unconfined compressive strength (UCS) from the drilling data. They found that using ML techniques to predict UCS was superior compared to the available empirical correlations 24 . Moreover, ML were used to calculate the oil production rate as a function of the choke parameters 26 .…”

Section: Applications Of Machine Learning In Geomechanicsmentioning

confidence: 99%

Machine learning application to predict in-situ stresses from logging data

Ibrahim

Gowida

Ali

et al. 2021

Sci Rep

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Determination of in-situ stresses is essential for subsurface planning and modeling, such as horizontal well planning and hydraulic fracture design. In-situ stresses consist of overburden stress (σv), minimum (σh), and maximum (σH) horizontal stresses. The σh and σH are difficult to determine, whereas the overburden stress can be determined directly from the density logs. The σh and σH can be estimated either from borehole injection tests or theoretical finite elements methods. However, these methods are complex, expensive, or need unavailable tectonic stress data. This study aims to apply different machine learning (ML) techniques, specifically, random forest (RF), functional network (FN), and adaptive neuro-fuzzy inference system (ANFIS), to predict the σh and σH using well-log data. The logging data includes gamma-ray (GR) log, formation bulk density (RHOB) log, compressional (DTC), and shear (DTS) wave transit-time log. A dataset of 2307 points from two wells (Well-1 and Well-2) was used to build the different ML models. The Well-1 data was used in training and testing the models, and the Well-2 data was used to validate the developed models. The obtained results show the capability of the three ML models to predict accurately the σh and σH using the well-log data. Comparing the results of RF, ANFIS, and FN models for minimum horizontal stress prediction showed that ANFIS outperforms the other two models with a correlation coefficient (R) for the validation dataset of 0.96 compared to 0.91 and 0.88 for RF, and FN, respectively. The three models showed similar results for predicting maximum horizontal stress with R values higher than 0.98 and an average absolute percentage error (AAPE) less than 0.3%. a20 index for the actual versus the predicted data showed that the three ML techniques were able to predict the horizontal stresses with a deviation less than 20% from the actual data. For the validation dataset, the RF, ANFIS, and FN models were able to capture all changes in the σh and σH trends with depth and accurately predict the σh and σH values. The outcomes of this study confirm the robust capability of ML to predict σh and σH from readily available logging data with no need for additional costs or site investigation.

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“…AI has been broadly applied in oil and gas industry because it has not only the capability to solve complicated issues, but it also represents them with a high accuracy 27 . Intelligent models were developed for various targets such as estimating the equivalent circulation density in real-time 28 – 30 , pore pressure estimation while drilling 31 , 32 , porosity prediction 33 , resistivity prediction 34 , predicting mud rheological properties 35 – 39 , predicting the unconfined compressive strength 40 , estimating the oil recovery factor 41 , bulk density log prediction 42 , 43 , well planning 44 , lithology classification 45 , fracture density estimation 46 , estimating the static elastic moduli 47 , 48 , Poisson’s ratio prediction 49 – 51 , and prediction of formation tops 52 .…”

Section: Introductionmentioning

confidence: 99%

Real-time prediction of formation pressure gradient while drilling

Abdelaal

Elkatatny

Abdulraheem

2022

Sci Rep

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Accurate real-time pore pressure prediction is crucial especially in drilling operations technically and economically. Its prediction will save costs, time and even the right decisions can be taken before problems occur. The available correlations for pore pressure prediction depend on logging data, formation characteristics, and combination of logging and drilling parameters. The objective of this work is to apply artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) to introduce two models to estimate the formation pressure gradient in real-time through the available drilling data. The used parameters include rate of penetration (ROP), mud flow rate (Q), standpipe pressure (SPP), and rotary speed (RS). A data set obtained from some vertical wells was utilized to develop the predictive model. A different set of data was utilized for validating the proposed artificial intelligence (AI) models. Both models forecasted the output with a good correlation coefficient (R) for training and testing. Moreover, the average absolute percentage error (AAPE) did not exceed 2.1%. For validation stage, the developed models estimated the pressure gradient with a good accuracy. This study proves the reliability of the proposed models to estimate the pressure gradient while drilling using drilling data. Moreover, an ANN-based correlation is provided and can be directly used by introducing the optimized weights and biases, whenever the drilling parameters are available, instead of running the ANN model.

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Unconfined compressive strength (UCS) prediction in real-time while drilling using artificial intelligence tools

Cited by 58 publications

References 30 publications

[Retracted] Prediction of the Least Principal Stresses Using Drilling Data: A Machine Learning Application

[Retracted] Prediction of the Least Principal Stresses Using Drilling Data: A Machine Learning Application

Machine learning application to predict in-situ stresses from logging data

Real-time prediction of formation pressure gradient while drilling

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