Improved well logs clustering algorithm for shale gas identification and formation evaluation

Abstract

The identification of lithology, fluid types, and total organic carbon content are of great priority in the exploration of unconventional hydrocarbons. As a new alternative, a further developed K-means type clustering method is suggested for the evaluation of shale gas formations. The traditional approach of cluster analysis is mainly based on the use of the Euclidean distance for grouping the objects of multivariate observations into different clusters. The high sensitivity of the L2 norm applied to non-Gaussian distributed measurement noises is well-known, which can be reduced by selecting a more suitable norm as distance metrics. To suppress the harmful effect of non-systematic errors and outlying data, the Most Frequent Value method as a robust statistical estimator is combined with the K-means clustering algorithm. The Cauchy-Steiner weights calculated by the Most Frequent Value procedure is applied to measure the weighted distance between the objects, which improves the performance of cluster analysis compared to the Euclidean norm. At the same time, the centroids are also calculated as a weighted average (using the Most Frequent Value method), instead of applying arithmetic mean. The suggested statistical method is tested using synthetic datasets as well as observed wireline logs, mud-logging data and core samples collected from the Barnett Shale Formation, USA. The synthetic experiment using extremely noisy well logs demonstrates that the newly developed robust clustering procedure is able to separate the geological-lithological units in hydrocarbon formations and provide additional information to standard well log analysis. It is also shown that the Cauchy-Steiner weighted cluster analysis is affected less by outliers, which allows a more efficient processing of poor-quality wireline logs and an improved evaluation of shale gas reservoirs.

A case study of petrophysical prediction using machine learning integrated with interval inversion in a tight sand reservoir in Egypt

“…Besides that, the MFV-cluster-assisted interval inversion shows the stability of the convergence of the data distance at 7.5%, which is lower than that of the normal inversion that converged to 10%. Figure (7) shows the data distance convergence for both the MFV-cluster-assisted interval inversion and the conventional interval inversion.…”

“…To overcome these disadvantages, this research proposed the use of an iterative algorithm called Most Frequent Value IOP Publishing doi:10.1088/1755-1315/1295/1/012008 2 (MFV) for automatic refining of the calculated distances as well as updating the location of the centroids. The MFV algorithm has been used in many applications to guarantee the stability of the convergence in different optimization problems [6,7]. The integration between the MFV and partitioning cluster produces the so-called MFV-clustering by which the exact obtained solution can result.…”

Robust reservoir identification by multi-well cluster analysis of wireline logging data

“…Non-hierarchical cluster analysis was used for assisting permeability prediction with transforming the well logs into electrofacies in dolomite and sandstone intervals in the Ogallah Field, USA [ 10 ], specifying the facies for a well in sandstone formation in West Africa before predicting the formation permeability [ 11 ], and the identification of heterogeneous carbonate reservoirs in a Southern Iraqi oilfield [ 12 ]. Other recent well log applications include improved electrofacies identification and lithology classification [ 13 , 14 ], assisting pseudo-well stochastic seismic inversion [ 15 ], automated layer-thickness determination for inversion procedures and estimation of typical log response values of hydrocarbon formations [ 16 ], clustering of incomplete core laboratory datasets [ 17 ], sweet spot identification and separation of different gas-bearing intervals in unconventional reservoirs [ [18] , [19] , [20] ]. As new alternative, machine learning tools can help to solve geophysical inverse problems.…”

“…This modification justifies a new name given for the method since the K-MFVs CA method can be considered as an improved variant of the traditional K-means clustering method. The idea of this clustering concept was first applied to wireline logs to identify clayey-shaly Hungarian coal formations [ 43 ], and to classify organic-rich shale formations [ 44 ]. We further improve the MFV based CA method at some points.…”

Meta-algorithm assisted interval inversion for petrophysical properties prediction

“…According to(Szabó et al, 2021;Liu et al, 2018;Szabó and Dobróka, 2020), and(Dobróka et al, 2016), in the interval inversion procedure, the objective function that must be reduced is a weighted function based on the least-squares criteria.𝐸(𝒎) = ∑ ∑ (…”