In the petroleum industry, drilling optimization
involves the selection of operating conditions for achieving
the desired depth with the minimum expenditure
while requirements of personal safety, environment protection,
adequate information of penetrated formations
and productivity are fulfilled. Since drilling optimization
is highly dependent on the rate of penetration (ROP), estimation
of this parameter is of great importance during
well planning. In this research, a novel approach called
‘optimized support vector regression’ is employed for making
a formulation between input variables and ROP. Algorithms
used for optimizing the support vector regression
are the genetic algorithm (GA) and the cuckoo search algorithm
(CS). Optimization implementation improved the
support vector regression performance by virtue of selecting
proper values for its parameters. In order to evaluate
the ability of optimization algorithms in enhancing SVR
performance, their results were compared to the hybrid
of pattern search and grid search (HPG) which is conventionally
employed for optimizing SVR. The results demonstrated
that the CS algorithm achieved further improvement
on prediction accuracy of SVR compared to the GA
and HPG as well. Moreover, the predictive model derived
from back propagation neural network (BPNN), which is
the traditional approach for estimating ROP, is selected
for comparisons with CSSVR. The comparative results revealed
the superiority of CSSVR. This study inferred that
CSSVR is a viable option for precise estimation of ROP.
Owing to high dependency of landslide stability to residual shear strength (RSS) of clay, provide a sophisticated strategy for modeling of this parameter is advantageous. This paper present strategy based upon fusing of optimized intelligence models for estimation of RSS of clay as a function of readily available data. The developed model is achieved through implementing two following steps. In the first step, two optimized models including optimized neural network, and optimized fuzzy logic are developed for estimation of RSS of clay. Optimizing method which implanted in predictive models for improving those performance is bat-inspired algorithm. In second step, committee machine (CM) is employed for combining outputs of aforementioned optimized models. Bat-inspired is incorporated in CM for determining optimal contribution of optimized elements in final prediction. The superior performance of CM rather than its elements is ascertain through those evaluation based on statistical criteria. Results of this study infer that proposed methodology provide an alternative way for making quantitative formulation between RSS of clay and its index properties.Keywords Residual shear strength (RSS) Á Optimized neural network (ONN) Á Optimized fuzzy logic (OFL) Á Committee machine (CM) Á Bat-inspired algorithm (BA)
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.