Application of Group Method of Data Handling via a Modified Levenberg-Marquardt Algorithm in the Prediction of Compressive Strength of Oilwell Cement with Reinforced Fly Ash Based on Experimental Data

Abstract

Summary The experimental design of well cement with durable compressive strength (CS) is challenging and time-consuming. The current research predicts CS using the enhanced group method of data handling via a modified Levenberg-Marquardt algorithm (GMDH-LM) with experimental data. Class F fly ash (CFFA) is used as a supplementary material to cement at various proportions. Experimental tests of CS, thermogravimetric (TG) analysis, rheology, and scanning electron microscopy (SEM) are applied. Experimental findings revealed that the addition of fly ash (FA) enhances CS with curing time as an outcome of pozzolanic action. CS for 20% FA reinforcement after curing for 28 days was 42.95 MPa, compared with 41.53 MPa for 50%. This indicates that a higher addition of FA lowers CS. The rheological findings revealed that FA enhanced the viscosity of the cement slurry. The SEM images demonstrated that the incorporation of CFFA with cement modified the contexture of hardened cement. Cement, water, oilwell cement (OWC), curing time, dispersant, and FA were assigned as input variables for GMDH-LM while CS from the experimental analysis was set as output. Machine learning (ML) findings indicated that GMDH-LM can effectively estimate the CS of OWC. GMDH-LM performed better than backpropagation neural network (BPNN), support vector machine (SVM), and normal GMDH models in predicting CS; it provided higher linearity during training as GMDH-LM gave R2 = 0.958, GMDH = 0.946, SVM = 0.925, BPNN = 0.897, and the least loss functions of mean square error (MSE) = 0.238, MSE = 1.685, MSE = 2.567, and MSE = 4.032, respectively. Similarly, good results were ascertained during testing GMDH-LM provided R2 = 0.928, GMDH = 0.907, SVM = 0.895, BPNN = 0.878, and the lowest loss functions of MSE = 0.304, MSE = 2.650, MSE = 3.494, and MSE = 5.678, respectively. Therefore, the comparative results of all experiments and predictions reveal that GMDH-LM can be deployed as an advanced approach for the estimation of cement hydration in oil and gas wells.

Field Telemetry Drilling Dataset Modeling with Multivariable Regression, Group Method Data Handling, Artificial Neural Network, and the Proposed Group-Method-Data-Handling-Featured Artificial Neural Network

“…To study these effects, the GMDH regression was considered and compared with the multivariable regression. The application of the GMDH network has been implemented in several fields [34][35][36][37][38][39][40][41][42][43][44]. The GMDH algorithm, as described in several references [34,35,46,53], utilizes multiple inputs to identify the best combination and generates a quadratic polynomial.…”

“…Among others, GMDH has been utilized for accurate log interval value estimation (Mohammed Ayoub (2014) [34]), permeability prediction by Alvin K. Mulashani (2019) [35] and Lidong Zhao (2023) [36], as well as permeability modeling and pore pressure analysis by Mathew Nkurlu (2020) [37]. Additionally, GMDH finds applications in cement compressive strength design (Edwin E. Nyakilla, 2023 [38]), rock deformation prediction (Li et al, 2020 [39]), bubble point pressure estimation by Fahd Saeed Alakbari (2022) and Mohammad Ayoub (2022) [40,41], gas viscosity determination, CO 2 emission modeling (Rezaei et al, 2020 and2018 [42,43]), the prediction of CO 2 adsorption by Zhou L. (2019) [44] and Li (2017) [45], forecasting stock indices, and modeling power and torque as demonstrated by Ahmadi (2015) [46] and Gao Guozhong (2023) [47], and the prediction of pore pressure by Mgimba (2023) [48].…”

Carbon Dioxide Capture under Low-Pressure Low-Temperature Conditions Using Shaped Recycled Fly Ash Particles

“…Tosun, Y.I [39] studied the use of fly ash as a construction material by testing the different strengths of the material including tensile, compressive, and shear strength. Nyakilla, E. et al [40] applied a modified modeling method to determine the characteristics of fly-ash-reinforced cement. The model is specific to the type of fly ash used since the fly ash varied significantly depending on the source of the fly ash and its precursor material.…”