Core log integration: a hybrid intelligent data-driven solution to improve elastic parameter prediction

Tariq, Zeeshan; Mahmoud, Mohamed; Abdulraheem, Abdulazeez

doi:10.1007/s00521-019-04101-3

Cited by 29 publications

(17 citation statements)

References 78 publications

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“…The performance of SVM depends on the tuning process of several parameters that need to be optimized in order to develop the desired predictive model with a high level of accuracy. SVM was recently introduced in the petroleum engineering field and has many applications there [40][41][42][43].…”

Section: Support Vector Machinesmentioning

confidence: 99%

New Computational Artificial Intelligence Models for Generating Synthetic Formation Bulk Density Logs While Drilling

et al. 2020

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Synthetic well log generation using artificial intelligence tools is a robust solution for situations in which logging data are not available or are partially lost. Formation bulk density (RHOB) logging data greatly assist in identifying downhole formations. These data are measured in the field while drilling by using a density log tool in the form of either a logging while drilling (LWD) technique or (more often) by wireline logging after the formations are drilled. This is due to operational limitations during the drilling process. Therefore, the objective of this study was to develop a predictive tool for estimating RHOB while drilling using an adaptive network-based fuzzy interference system (ANFIS), functional network (FN), and support vector machine (SVM). The proposed model uses the mechanical drilling constraints as feeding input parameters, and the conventional RHOB log data as an output parameter. These mechanical drilling parameters are usually measured while drilling, and their responses vary with different formations. A dataset of 2400 actual datapoints, obtained from a horizontal well in the Middle East, were used to build the proposed models. The obtained dataset was divided into a 70/30 ratio for model training and testing, respectively. The optimized ANFIS-based model outperformed the FN- and SVM-based models with a correlation coefficient (R) of 0.93, and average absolute percentage error (AAPE) of 0.81% between the predicted and measured RHOB values. These results demonstrate the reliability of the developed ANFIS model for predicting RHOB while drilling, based on the mechanical drilling parameters. Subsequently, the ANFIS-based model was validated using unseen data from another well within the same field. The validation process yielded an AAPE of 0.97% between the predicted and actual RHOB values, which confirmed the robustness of the developed model as an effective predictive tool for RHOB.

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Section: Support Vector Machinesmentioning

confidence: 99%

New Computational Artificial Intelligence Models for Generating Synthetic Formation Bulk Density Logs While Drilling

et al. 2020

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“…CI can address such complications with relative ease . Some of the domains of the petroleum engineering in which CI techniques brought new innovations include porosity-permeability predictions (Abdulraheem et al 2007;El-Sebakhy et al 2012;Nooruddin et al 2013;Helmy et al 2013;Anifowose et al 2013Anifowose et al , 2014Anifowose et al , 2017, hydraulic flow unit identification (Shujath Ali et al 2013), rock mechanical parameters estimation (Yang and Rosenbaum 2002;Sonmez et al 2004;Abdulraheem et al 2009;Cevik et al 2011;Tariq et al 2018b), missing petrophysical well logs estimation (Tariq et al 2019), welltesting parameters estimation (Artun 2017;Bazargan and Adibifard 2017), asphaltene and wax precipitation predictions (Rezaian et al 2010;Adeyemi and Sulaimon 2012;Fattahi et al 2015;Alimohammadi et al 2017), water saturation prediction (Adebayo et al 2015;Bageri et al 2015;Baziar et al 2016Baziar et al , 2018Khan et al 2018), gas compressibility factor (Mohagheghian et al 2015;Tariq and Mahmoud 2019), oil well drilling rate of penetration optimization (Gidh et al 2012), and many other oil and gas applications (Ashena et al 2010;Jahanandish et al 2011;Asoodeh 2013;Rammay and Abdulraheem 2016).…”

Section: Introductionmentioning

confidence: 99%

Real-time prognosis of flowing bottom-hole pressure in a vertical well for a multiphase flow using computational intelligence techniques

Tariq

Mahmoud

Abdulraheem

2019

J Petrol Explor Prod Technol

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An accurate prediction of well flowing bottom-hole pressure (FBHP) is highly needed in petroleum engineering applications such as for the field production optimization, cost per barrel of oil reduction, and quantification of workover remedial operations. A good number of empirical correlations and mechanistic models exist in the literature and are frequently used in oil industry to estimate FBHP. But majority of the empirical models were developed under a laboratory scale and are therefore inaccurate when scaled up for the field applications. The objective of this study is to present a new computational intelligence-based model to predict FBHP for a naturally flowing vertical well with multiphase flow. The present study shows that the accuracy of FBHP estimation using PSO-ANN is better than the conventional ANN model. A small average absolute percentage error of less than 2.1% is observed with the proposed model, while comparing the previous empirical correlations and mechanistic models on the same data gives more than 15% error. The new model is trained on a surface production data, which makes the prediction of FBHP in a real time. A group trend analysis tests were also carried out to assure that the proposed model is accurately capturing the underline physics behind the problem.

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“…These parameters are very important in describing the elastic behavior of rock [8]. These parameters are crucial for avoiding many problems and minimizing the risks associated with well drilling operations [5,[9][10][11]. An accurate estimation of these parameters helps to solve wellbore instability issues, identify the safe mud-weight window while drilling, and optimize the fracture geometry and orientation, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, the profiles of dynamic Poisson's ratio (PR dynamic ) are estimated using sonic log data, which are calibrated by determining the difference between PR dynamic and PR static of the measured core data using Equation (1). All dynamic Poisson's ratio values can then be adjusted by adding this difference, resulting in a shift in the PR dynamic profile towards the actual values of PR static [11,15,17,21]. However, the accuracy of this technique is limited to the interval which the core samples represent [5,14].…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks

et al. 2019

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Rock mechanical properties play a key role in the optimization process of engineering practices in the oil and gas industry so that better field development decisions can be made. Estimation of these properties is central in well placement, drilling programs, and well completion design. The elastic behavior of rocks can be studied by determining two main parameters: Young’s modulus and Poisson’s ratio. Accurate determination of the Poisson’s ratio helps to estimate the in-situ horizontal stresses and in turn, avoid many critical problems which interrupt drilling operations, such as pipe sticking and wellbore instability issues. Accurate Poisson’s ratio values can be experimentally determined using retrieved core samples under simulated in-situ downhole conditions. However, this technique is time-consuming and economically ineffective, requiring the development of a more effective technique. This study has developed a new generalized model to estimate static Poisson’s ratio values of sandstone rocks using a supervised artificial neural network (ANN). The developed ANN model uses well log data such as bulk density and sonic log as the input parameters to target static Poisson’s ratio values as outputs. Subsequently, the developed ANN model was transformed into a more practical and easier to use white-box mode using an ANN-based empirical equation. Core data (692 data points) and their corresponding petrophysical data were used to train and test the ANN model. The self-adaptive differential evolution (SADE) algorithm was used to fine-tune the parameters of the ANN model to obtain the most accurate results in terms of the highest correlation coefficient (R) and the lowest mean absolute percentage error (MAPE). The results obtained from the optimized ANN model show an excellent agreement with the laboratory measured static Poisson’s ratio, confirming the high accuracy of the developed model. A comparison of the developed ANN-based empirical correlation with the previously developed approaches demonstrates the superiority of the developed correlation in predicting static Poisson’s ratio values with the highest R and the lowest MAPE. The developed correlation performs in a manner far superior to other approaches when validated against unseen field data. The developed ANN-based mathematical model can be used as a robust tool to estimate static Poisson’s ratio without the need to run the ANN model.

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Core log integration: a hybrid intelligent data-driven solution to improve elastic parameter prediction

Cited by 29 publications

References 78 publications

New Computational Artificial Intelligence Models for Generating Synthetic Formation Bulk Density Logs While Drilling

New Computational Artificial Intelligence Models for Generating Synthetic Formation Bulk Density Logs While Drilling

Real-time prognosis of flowing bottom-hole pressure in a vertical well for a multiphase flow using computational intelligence techniques

A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks

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