Classification of Hard-to-Recover Hydrocarbon Reserves of Kazakhstan with the Use of Fuzzy Cluster-Analysis

Akhmetov, D. A.; Efendiyev, G. M.; Karazhanova, M. K.; Koylibaev, B. N.

doi:10.1007/978-3-030-04164-9_114

Cited by 3 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The work (Akhmetov et al, 2018) gives a graphical representation of boundaries separating classes-terms-sets of oil density, oil viscosity and reservoir permeability. In this case, light oil has density of 700-815 kg/m 3 , moderately heavy oil has density of 800-890 kg/m 3 ; heavy oil has density of 892-910 kg/m 3 ; and extra heavy oil has density ranging between 910 and 933 kg/m 3 and low viscous is (0,17-6,9) MPa.s; moderately viscous -(8,6-55) mPa.s; viscous -(55-160) MPa.s; 160 MPa.s and higher.…”

Section: T a B L Ementioning

confidence: 99%

“…Over the recent years, the issues of cluster analysis (sometimes also known as the issues of automated classification) have been used extensively in such spheres as economics, sociology, medicine, geology, oilfield development and other industries, where we deal with sets of objects of arbitrary nature described as x = {x1, x2, …, xN} vectors that have to be automatically divided into groups of homogeneous objects according to the criteria of similarities (within a homogeneous object or a cluster) and differences between these objects. Much has been published on psychological, sociological, economic, G&G, well drilling, oil and gas field development studies (Akhmetov et al, 2018;Koilybayev et al, 2018;Зайченко и Гончар, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…The results obtained facilitate decision-making regarding lost circulation prevention and a timely response to eliminate its consequences. In most cases, specifically when it comes to oilfield practices (Efendiyev et al, 2018;Akhmetov et al, 2018;Koilybayev et al, 2018;Зайченко и Гончар, 2007;Bezdek et al, 1984), a partition must be done in such a way as to determine the degree to which each object belongs to, i.e. the degree of recognition.…”

Section: Introductionmentioning

confidence: 99%

“…The following three indicators were selected for our cluster analysis: oil viscosity, oil density, and reservoir permeability. The fuzzy cluster analysis produced in total four classes, with each class reflecting the degree of oil recovery complexity (Akhmetov et al, 2018):…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluating the Degree of Complexity of Tight Oil Recovery Based on the Classification of Oils

et al. 2020

View full text Add to dashboard Cite

The article discusses the results of the use of cluster analysis in assessing the degree of oil recovery complexity and its impact on the performance indicator. For this purpose, clustering was performed using a fuzzy cluster analysis algorithm. It should be noted that along with the deposits of heavy and highly viscous oils, a large share of hard-to-recover reserves is also confined to conditions with very low reservoir permeability values. Data on viscosity, oil density and oil permeability of in-situ conditions from various fields of Kazakhstan are collected. Using the results of this classification, a statistical analysis of indicators of various types of hard-torecover oils was performed. In the process of analysis, a generalized characteristic was determined for each class of oil, including viscosity, oil density and reservoir permeability. The generic characteristic is a linear transformation of the three characteristics. The results were subjected to statistical processing. At the same time, an attempt was made to establish and analyze the relationship between the degree of recovery complexity of hard-to-recover oils and oil recovery coefficient. In the course of the analysis, the average values of the oil recovery coefficient and the index of the degree of recovery complexity of hard-to-recover oil within each cluster were calculated and the relationship between them was plotted. The observed dependence, built on averaged points, is close to a power law, and, as one would expect, with an increase in the degree of oil recovery complexity, the oil recovery coefficient falls. The obtained estimates of the degree of oil recovery complexity allow us to rank different types of oils by their viscosity, density and reservoir permeability, which can be used to compare types of hard-to-recover oils by the value of the quality indicator. Methods to solve the problem of hard-to-remove high-viscosity and heavy oils should be aimed at reducing the viscosity of oil in the reservoir: injection of hot water / steam into the reservoir, the use of electric heaters, etc. Purpose. Assessment of the degree of oil recovery complexity and its impact on the efficiency of field development. The technique. The solution of the tasks set in the work was carried out on the method of mathematical statistics and the theory of fuzzy sets. In this case, the methods of processing the results, the correlation analysis, and the algorithm of fuzzy cluster analysis were used. Results. As a result of studies, 4 classes were obtained, each of which characterizes the degree of oil recovery complexity, a parameter was proposed for quantifying the degree of complexity, including oil density and viscosity, reservoir permeability, a relationship between this parameter and oil recovery coefficient was obtained. Scientific novelty. A classification of hard-to-recover reserves based on a fuzzy cluster analysis has been performed, and a parameter has been proposed for quantifying the degree of oil recovery complexity, a relationship has been obtained that allows judging the oil recovery by the degree of oil recovery complexity. Practical significance. The results obtained make it possible to classify hard-to-recover reserves and make decisions on the choice of methods for influencing the reservoir in various geological conditions.

show abstract

Section: T a B L Ementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%