Applying Statistical Methods to Assess Fractured Reservoirs Relying on Field Data
The presence of fractures in reservoirs can have both a negative and a positive impact on the technical and economic indicators of field development and thus an early classification of fractures is an important requisite. Identifying the type of a fractured reservoir at the initial stages of develop...
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Georesursy Ltd.
2025-04-01
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| author | A. I. Shchekin |
| author_facet | A. I. Shchekin |
| author_sort | A. I. Shchekin |
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| description | The presence of fractures in reservoirs can have both a negative and a positive impact on the technical and economic indicators of field development and thus an early classification of fractures is an important requisite. Identifying the type of a fractured reservoir at the initial stages of development is a clue to selecting an optimal type of model and field development system. The paper evaluates the applicability of the cumulative production indicator and the productivity index of wells to determine the type of fractured reservoir with the help of a statistical method for analyzing the Lorenz curve and the Gini coefficient (as applied to determine the impact of fractures) with small data samples and at the initial stages of development. A method of mathematical statistics namely the bootstrap method is used in this paper in order to study the fracture impact coefficient for a small number of wells. This method is based on the repeated generation of random samples multitude from the original data set and their subsequent statistical analysis. Modeling of samples was carried out by means of a random number generator available in spreadsheets. The results of a research proved that the use of indicators such as cumulative production and productivity index to identify fractured reservoirs with a small number of wells produced the comparable results. To increase the reliability of classification for a small number of wells, a data sample is required that will most fully describe the field. It is possible to obtain a representative sample of data for an objective analysis of the distribution and influence of fracture systems by placing wells covering the entire area of the field. In the early stages of development, due to the low production volumes and short periods of well operation, it is recommended to use the productivity index for the analysis. |
| format | Article |
| id | doaj-art-9d53467fa3044b29a3d4bc73e8506b93 |
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| issn | 1608-5043 1608-5078 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Georesursy Ltd. |
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| spelling | doaj-art-9d53467fa3044b29a3d4bc73e8506b932025-07-21T10:35:36ZengGeoresursy Ltd.Georesursy1608-50431608-50782025-04-0127127528310.18599/grs.2025.1.3310Applying Statistical Methods to Assess Fractured Reservoirs Relying on Field DataA. I. Shchekin0North-Caucasus Federal UniversityThe presence of fractures in reservoirs can have both a negative and a positive impact on the technical and economic indicators of field development and thus an early classification of fractures is an important requisite. Identifying the type of a fractured reservoir at the initial stages of development is a clue to selecting an optimal type of model and field development system. The paper evaluates the applicability of the cumulative production indicator and the productivity index of wells to determine the type of fractured reservoir with the help of a statistical method for analyzing the Lorenz curve and the Gini coefficient (as applied to determine the impact of fractures) with small data samples and at the initial stages of development. A method of mathematical statistics namely the bootstrap method is used in this paper in order to study the fracture impact coefficient for a small number of wells. This method is based on the repeated generation of random samples multitude from the original data set and their subsequent statistical analysis. Modeling of samples was carried out by means of a random number generator available in spreadsheets. The results of a research proved that the use of indicators such as cumulative production and productivity index to identify fractured reservoirs with a small number of wells produced the comparable results. To increase the reliability of classification for a small number of wells, a data sample is required that will most fully describe the field. It is possible to obtain a representative sample of data for an objective analysis of the distribution and influence of fracture systems by placing wells covering the entire area of the field. In the early stages of development, due to the low production volumes and short periods of well operation, it is recommended to use the productivity index for the analysis.https://www.geors.ru/jour/article/view/296fractured reservoirsclassification of fractured reservoirsfracture impact coefficientlorenz curvebootstrapping |
| spellingShingle | A. I. Shchekin Applying Statistical Methods to Assess Fractured Reservoirs Relying on Field Data Georesursy fractured reservoirs classification of fractured reservoirs fracture impact coefficient lorenz curve bootstrapping |
| title | Applying Statistical Methods to Assess Fractured Reservoirs Relying on Field Data |
| title_full | Applying Statistical Methods to Assess Fractured Reservoirs Relying on Field Data |
| title_fullStr | Applying Statistical Methods to Assess Fractured Reservoirs Relying on Field Data |
| title_full_unstemmed | Applying Statistical Methods to Assess Fractured Reservoirs Relying on Field Data |
| title_short | Applying Statistical Methods to Assess Fractured Reservoirs Relying on Field Data |
| title_sort | applying statistical methods to assess fractured reservoirs relying on field data |
| topic | fractured reservoirs classification of fractured reservoirs fracture impact coefficient lorenz curve bootstrapping |
| url | https://www.geors.ru/jour/article/view/296 |
| work_keys_str_mv | AT aishchekin applyingstatisticalmethodstoassessfracturedreservoirsrelyingonfielddata |