Machine learning technique for the prediction of shear wave velocity using petrophysical logs

Anemangely, Mohammad; Ramezanzadeh, Ahmad; Amiri, Hamed; Hoseinpour, Seyed-Ahmad

doi:10.1016/j.petrol.2018.11.032

Cited by 118 publications

(20 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On other hand, ML appeared to be a successful tool capable of constructing a relationship between log curves based on their effective features for DTS prediction to evaluate the reservoir properties. Many researchers have recently used ML for predicting the DTS curve, that is, Bukar et al (2019), Anemangely et al (2019), Miah (2021), Gamal et al (2022), Gupta et al (2019), andLiu et al (2021). Due to complex reservoir attributes and limited data set, ML is a critical and optimized tool in the most productive LIB for predicting the DTS curve.…”

Section: Resultsmentioning

confidence: 99%

Supervised machine learning for predicting shear sonic log (DTS) and volumes of petrophysical and elastic attributes, Kadanwari Gas Field, Pakistan

et al. 2022

View full text Add to dashboard Cite

Shear sonic log (DTS) availability is vital for litho-fluid discrimination within reservoirs, which is critical for field development and production. For certain reasons, most of the wells in the Lower Indus Basin (LIB) lack DTS logs, which are modeled using conventional techniques based on empirical relations and rock physics modeling. However, in their extensive computation, these approaches need assumptions and multiple prerequisites, which can compromise the true reservoir characteristics. Machine learning (ML) has recently emerged as a robust and optimized technique for predicting precise DTS with fewer input data sets. To predict the best DTS log that adheres to the geology, a comparison was made between three supervised machine learning (SML) algorithms: random forest (RF), decision tree regression (DTR), and support vector regression (SVR). Based on qualitative statistical measures, the RF stands out as the best algorithm, with maximum determination of correlation (R2) values of 0.68, 0.86, 0.56, and 0.71 and lower mean absolute percentage error (MAPE) values of 4.5, 2.01, 4.79, and 4.65 between the modeled and measured DTS logs in Kadanwari-01, -03, -10, and -11 wells, respectively. For detailed reservoir characterization, the RF algorithm is further employed to generate elastic attributes such as P-impedance (Zp), S-impedance (Zs), lambda-rho (λρ), mu-rho (μρ), as well as petrophysical attributes such as effective porosity (PHIE) and clay volumetric (Vcl) utilizing seismic and well data. The resultant attributes helped to establish a petro-elastic relationship delineated at the reservoir level. Possible gas zones were determined by zones with high PHIE (8%–10%) and low values of other attributes like Vcl (30%–40%), Zp (10,400–10,800 gm/cc*m/s), and Zs (6,300–6,600 gm/cc*m/s). The potential bodies are also validated by low λρ (27–30 GPa*g/cc) cross ponding to higher μρ (38–44 GPa*g/cc).

show abstract

Section: Resultsmentioning

confidence: 99%

Supervised machine learning for predicting shear sonic log (DTS) and volumes of petrophysical and elastic attributes, Kadanwari Gas Field, Pakistan

et al. 2022

View full text Add to dashboard Cite

show abstract

“…4 with zonation tops. The seven parameters (TVD, DT, GR, CAL, NPHI, RHOB, and RD) were chosen for the input layer of the ANN based on their demonstrated effects on DTS estimation by various researchers (Akhundi et al, 2014;Anemangely et al, 2019;Shi and Zhang, 2021;Asoodeh and Bagheripour, 2011;Maleki et al, 2014;Rezaee et al, 2007). These studies indicated that seven parameters could be classified into direct and indirect effects on DTS.…”

Section: Methodsmentioning

confidence: 99%

“…A number of previous studies that used various AI methods examined the effects of parameters on DTS prediction. Utilized AI methods examples as artificial neural network (ANN), feedforward back propagation neural network (FFBP), The Least Square Support Vector Machine (LSSVM) in conjunction with Particle Swarm Optimization (PSO), the Genetic Algorithm (GA), and Cuckoo Optimization Algorithm (COA), fuzzy logic, and neuro-fuzzy are powerful methods for prediction DTS in term of shear wave velocity (Vs) where the last is reciprocal of DTS (Rezaee et al, 2007;Tabari et al, 2011;Tariq et al 2016;Hadi and Nygaard, 2018;Anemangely et al, 2019;Al Ghaithi and Prasad, 2020;Shi and Zhang, 2021). The better decisions for adopting AI techniques are approved based on massive historical datasets available in the petroleum industry (Mohaghegh, 2000).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Sonic Shear Wave Using Artificial Neural Network

Naji

Abdul-Majeed

Alhuraishawy³

2022

IGJ

View full text Add to dashboard Cite

Shear wave time is an important parameter participating in the calculation of rock mechanics properties. Many evaluation processes including wellbore stability analysis and sand production prediction are based on rock mechanics properties so these processes are directly related to the estimation of shear wave time. Several empirical correlations have been developed to predict shear wave time using regression analysis and artificial neural network techniques for the estimation of relationships between a dependent variable and one or more independent variables for certain conditions of the reservoir. However, they are not appropriate for reservoirs with different conditions as well as all effective parameters are not considered in previous relationships. In this study, the artificial neural network is adopted for predicting shear wave time using datasets consisting of 1922 data points for a certain directional oil well from Iraqi Fauqi oil field wells. Two sets of input parameters are tried: the first trial includes the readings of seven logs (Gamma-ray, caliper, compressional sonic wave, density, neutron, deep resistivity, true vertical depth), while the second trial includes the azimuth and the inclination angles in addition to the above seven readings. The optimum structure for both datasets is obtained using 12 neurons in a single hidden layer (ANN-7-12-1 and ANN-9-12-1). The statistical results reveal that an improvement is achieved when the well azimuth and inclination are included in the ANN model. A mathematical model with high performance using an artificial neural network has been developed. The mean square error and the determination coefficient for the developed model were 14.22 and 0.952 for ANN-7-12-1, while they were 9.62 and 0.966 for ANN-9-12-1, respectively. This study presents a simple mathematical model for further determination of shear wave velocities using ANN techniques which can be then integrated with the existent petroleum software programs.

show abstract

“…Recently, ML methods have become popular and been widely used, because they are inexpensive, fast track, and practical without restrictions concerning the horizontal or deviation position of the wells [16]. These methods have been used in many studies to predict the shear wave velocity by employing a different range of artificial intelligent methods, such as Artificial Neural Networks (ANNs) [17,18], Support Vector Regression (SVR) and neural networks [19][20][21], soft computing [22], machine learning, and colonyfuzzy inference systems [23,24]. The results of these studies show that using AI methods to predict shear wave velocity is reliable and favorable.…”

Section: Introductionmentioning

confidence: 99%

A Fully-Self-Adaptive Harmony Search GMDH-Type Neural Network Algorithm to Estimate Shear-Wave Velocity in Porous Media

et al. 2022

View full text Add to dashboard Cite

Shear wave velocity (VS) is one of the most important parameters in deep and surface studies and the estimation of geotechnical design parameters. This parameter is widely utilized to determine permeability and porosity, lithology, rock mechanical parameters, and fracture assessment. However, measuring this important parameter is either impossible or difficult due to the challenges related to horizontal and deviation wells or the difficulty in reaching cores. Artificial Intelligence (AI) techniques, especially Machine Learning (ML), have emerged as efficient approaches for dealing with such challenges. Therefore, considering the advantage of the ML, the current research proposes a novel Fully-Self-Adaptive Harmony Search—Group Method of Data Handling (GMDH)-type neural network, named FSHS-GMDH, to estimate the VS parameter. In this way, the Harmony Memory Consideration Rate (HMCR) and Pitch Adjustment Rate (PAR) parameters are calculated automatically. A novel method is also introduced to adjust the value of the Bandwidth (BW) parameter based on the cosine wave and each decision variable values. In addition, a variable-size harmony memory is proposed to enhance both the diversification and intensification. Our proposed FSHS-GMDH algorithm quickly explores the problem space and exploits the best regions at the late iterations. This algorithm allows for the training of the prediction model based on the P-wave velocity (VP) and the bulk density of rock (RHOB). Applying the proposed algorithm to a carbonate petroleum reservoir in the Persian Gulf demonstrates that it is capable of accurately estimating the VS parameter better than state-of-the-art machine learning methods in terms of the coefficient of determination (R2), Mean Square Error (MSE), and Root Mean Square Error (RMSE).

show abstract

Machine learning technique for the prediction of shear wave velocity using petrophysical logs

Cited by 118 publications

References 21 publications

Supervised machine learning for predicting shear sonic log (DTS) and volumes of petrophysical and elastic attributes, Kadanwari Gas Field, Pakistan

Supervised machine learning for predicting shear sonic log (DTS) and volumes of petrophysical and elastic attributes, Kadanwari Gas Field, Pakistan

Prediction of Sonic Shear Wave Using Artificial Neural Network

A Fully-Self-Adaptive Harmony Search GMDH-Type Neural Network Algorithm to Estimate Shear-Wave Velocity in Porous Media

Contact Info

Product

Resources

About