Analysis and evaluation of the influence of the geological-physical and technological characteristics of deposits on the efficiency indicators of geological and technical measures

Жомарт Кайржанович Жантурин; Murat N Abishev; Мурат Николаевич Абишев; Абишев Мурат Николаевич; Yessengeldi U Arystanaliyev; Есенгелди Утешович Арстаналиев; Арстаналиев Есенкелди Утешович; Zhanylsyn K Zaidemova; Жанылсын Калидуллаевна Зайдемова; Жанылсын Калидуллаевна; Shokhan M Medetov; Шокан Медетович Медетов; Медетов Шокан Медетович

doi:10.54859/kjogi109026

Анализ и оценка влияния геолого-физических и технологических характеристик месторождения на показатели эффективности геолого-технических мероприятий

Авторы: Жантурин Ж.К.¹, Абишев М.Н.¹, Арстаналиев Е.У.¹, Zaidemova Ж.К.¹, Медетов Ш.М.¹
Учреждения:
1. НАО "Атырауский университет нефти и газа имени Сафи Утебаева"
Раздел: Научные обзоры
URL: https://vestnik-ngo.kz/2707-4226/article/view/109026
DOI: https://doi.org/10.54859/kjogi109026
ID: 109026

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Аннотация

АННОТАЦИЯ

Обоснование. Поддержание и повышение эффективности разработки нефтяных месторождений и интенсификации добычи нефти напрямую связано с уровнем применения геолого-технических мероприятий (ГТМ) в производстве. Точная и обоснованная реализация конкретных видов ГТМ зависит от системного подхода к их выбору. Однако на практике подбор ГТМ часто осуществляется без достаточного научного анализа, на основе неполной информации, полученной в ходе эксплуатации. В результате при переносе методов на другие месторождения с похожими геолого-физическими характеристиками их эффективность нередко оказывается ниже ожидаемой. Поэтому задача корректного выбора ГТМ, разработки научно-обоснованных методов прогнозирования результатов и последующей оценки эффективности остается актуальной.

Цель. Оценить эффективность ГТМ на основе геолого-физических и технологических данных конкретных месторождений; обеспечить возможность прогнозирования и оценки эффективности ГТМ в условиях недостатка информации; разработать математический инструментарий для таких прогнозов.

Материалы и методы. В исследовании использованы материалы, собранные на месторождениях Западного Казахстана (Тенгиз и Западная Прорва). Были выделены 16 показателей, определяющих эффективность методов повышения нефтеотдачи (толщина пласта, нефтенасыщенный интервал и др.), а также 5 ключевых критериев для оценки эффективности (коэффициент эффективности, дополнительная добыча нефти и др.). Для снижения размерности входных переменных применялся метод главных компонент (PCA). Ввиду многокритериальности оценки эффективности ГТМ, дополнительно использовалась теория нечетких множеств.

Результаты. На основе исходных данных месторождений были определены ключевые показатели, влияющие на эффективность ГТМ, и построены их математические модели. С помощью PCA 16 входных переменных были агрегированы в определенные факторы с сохранением полной информативности. Применение теории нечетких множеств позволило разработать методику прогнозирования технологической эффективности каждого типа ГТМ в условиях исходной неопределенности.

Заключение. Предложенный методологический подход является эффективным инструментом для системного анализа, прогнозирования и оптимизации программ геолого-технических мероприятий. Его внедрение снижает риск ошибочных технологических решений и способствует росту коэффициента нефтеотдачи.

Ключевые слова

геолого-технические мероприятия, метод главных компонент, показатели эффективности ГТМ, теория нечетких множеств, прогнозирование добычи нефти.

Полный текст

Introduction

Recent studies aimed at improving the efficiency of field development and increasing oil production rates highlight the use of a wide variety of geological and technical measures (GTMs) in mineral resource exploitation. The effectiveness of GTM implementation depends on the optimal combination of physical-geological, technological, and operational parameters that collectively define the requirements for each GTM type.

In practical production settings—whether for specific oil fields, strata, or wells—the choice of geological and technological measures, their parameters, and their technical and economic justification are typically made by geological departments within Oil and Gas Production Directorates (OGPD), drawing on extensive operational experience. For instance, at the Tengiz, Batys Prorva, Botakan, and Karsak fields, a cumulative 25,312.2 thousand tonnes of oil were produced as a result of GTMs [1].

Despite the expertise and qualifications of OGPD staff, the selection of sites, GTMs, and relevant technologies is often performed in an ad hoc manner, lacking alignment with the true geological and technological conditions of the fields. Consequently, introducing established GTMs into other reservoirs within the same field does not always yield high efficiency [2].

The causes of such inefficiencies typically include: the absence of a unified methodology for integrating physical-geological, technological, and production indicators that fully describe the implementation of GTMs; the lack of methods for techno-economic assessment of individual GTMs in particular cases; and insufficient approaches for predictive evaluation of relative efficiency under varying geological and technological conditions. These challenges are further exacerbated by heterogeneous and uncertain information, which can distort assessments and decision outcomes.

Analyzing and synthesizing core metrics of well intervention efficiency

Lately, the emergence of advanced methodologies addressing these intricacies has yielded more sophisticated outcomes. Designing diverse models and tailored software by merging geological, geophysical, and operational data allows for the optimization of engineering decisions [3]. Currently, several software systems have been developed that support the assessment of both technological and economic efficiency of GTMs, as well as the analysis of their performance in relation to geological and production requirements [4–7].

The diversity of GTM types, the range of actual and potential application conditions, and the variability of geological settings—when considered alongside accumulated operational experience—make it scientifically valuable to conduct comparative analyses of GTM effectiveness, not only for measures already implemented but also for hypothetical scenarios. In other words, the effectiveness of GTMs applied under specific conditions should be evaluated against predicted outcomes for alternative measures.

Such evaluations require models that explicitly link efficiency indicators to the geological and physical parameters characterizing each GTM. To develop these models, data on GTMs implemented at various fields were collected and analyzed. The following parameters were identified as key determinants of GTM effectiveness, as detailed in [8]: • total formation thickness, m (x₁);

oil-saturated layer thickness, m (x₂);
fractured oil-saturated layer thickness, m (x₃);
sand coefficient (x₄);
porosity, fraction (x₅);
permeability, Kpr∙10⁻³, μm² (x₆);
oil viscosity, m Pa∙s (reservoir conditions) (x₇);
oil density, t/m³ (x₈);
gas content, m³/t (x₉);
initial oil saturation, fraction (x₁₀);
formation temperature, °C (x₁₁);
paraffin content in oil, % (x₁₂);
sulfur content in oil, % (x₁₃);
oil production rate before geological and technical measures, t/day (x₁₄);
fluid production rate before geological and technical measures, t/day (x₁₅);
water cut before geological and technical measures, % (x₁₆) [8].

To evaluate how successfully a well intervention performs, five key metrics are tracked:

the lifespan of the treatment's impact in days (Y₁);
cumulative incremental oil yield in tonnes, t (Y₂);
and the specific boost in oil flow rate (Y₃);
additionally, the post-intervention production performance is analyzed through the final oil flow rate (Y₄);
and the resulting water cut percentage (Y₅) [8].

Consequently, the baseline data matrix for analyzing any given operational technique comprises 16 independent variables mapped against these five performance indicators. A corresponding table is prepared for each GTM type (e.g., Near-Wellbore Zone Treatment + Perforation, Acidizing + Perforation, etc.). To avoid overloading the article with excessive data, only the results for the OCP+perforation type are shown (Table 1). As the primary database [8] matches that used in the authors’ previous work, some indicators may repeat for certain conditional layers (e.g., 3, 4, 3-1, 4-3, etc.), further validating the research methodology for GTM efficiency. Tables for other GTM types are compiled in a similar manner [8].

Table 1 – Values of the defined attributes describing geological requirements and the corresponding values of the efficiency indicators for the GTM (Near-Wellbore Zone Treatment + Perforation) type.

Subsequent data synthesis utilized an evaluation framework designed for well intervention performance. This system processes engineering, geophysical, and operational attributes that thoroughly capture the reservoir conditions where specific treatment types are deployed. Within this framework, the baseline data are transformed specifically to streamline and minimize the overall input dimension.

Transformation [4], in accordance with the literature, the principal component method [4, 8] was implemented by applying the principal component method [4, 8]. The principal component method has been widely used in solving problems in geology [8, 9, 10, 11, 12]. The primary objective in this case is to compress the spatial volume of the available signals and transform it into a factor space. In this case, the number of transformed factors is considerably fewer than the number of original variables. In other words, countless variables containing a large amount of information must be replaced by a smaller number of more meaningful factors.

In general, this objective is achieved as follows.

The primary objective of the principal component method is to reduce the number of variables whilst preserving the complete information contained in the data. This is achieved by performing a linear transformation of the original random variable ???? (????1,……, ????????; i.e. ???? = 16 ) into a new set of random variables???? (, … . , ) that possess the specified statistical properties. Typically, the number of ???? is considerably smaller than the number of ????.

The main objective of the principal component method is to reduce the number of variables as much as possible whilst preserving the content of the information obtained; in other words, the number of variables is reduced, but the amount of information they provide remains the same. This method enabled us to reduce the 16 features for the GTM to six factors, while fully preserving the necessary information [4, 8].

However, despite its advantages, this method is labour-intensive, requires highly complex work, and cannot clearly demonstrate the dependence of the resulting factors on the corresponding indicators.

In this case, the method recently employed in economic research is both interesting and accessible. According to this method, the importance shares of the composite indicators are calculated for the newly formed factors.

Physical laws are also taken into account when constructing the factors. For example, the permeability coefficient, the fractured oil-saturated layer and the oil viscosity are combined to obtain the hydraulic conductivity coefficient (????ℎ⁄????), the total thickness of the formation, the oil-saturated layer and the fractured oil-saturated layer are combined into a dimensionless quantity of the form , where , and denote, respectively, the total formation thickness, the oil-saturated layer and the fractured oil-saturated layer [8].

Some variables, as mentioned above, have been combined through a linear transformation of the form .

In the same way, the paraffin content and sulphur content were combined, as were the oil and liquid debits. In the expression given,a???? represents the fractional coefficients that take into account the proportions of the variables included in the expression.

Thus, new variables of the following form were obtained [8]:

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

We present the transformed variable values of the data given in Table 1 in Table 2

Table 2 – Values of the transformed input variables for the GTM types (Near-Wellbore Zone Treatment + Perforation) according to patterns 1–9

In expressions (4)–(6), the fractional coefficients are determined by dividing the sum of the values of the variable under consideration by the total sum of the values of that variable and the comparison variable.

A correlation analysis was then conducted on the transformed data presented in Table 2. This analysis resulted in the derivation of multiple correlation equations and their corresponding statistical estimates. Specialized software was employed for this purpose. Each resulting equation expresses a specific efficiency indicator as a function of the selected factors:

(10)

The parameters of these equations are summarized in Table 3. This table presents results not only for the GTM Near-Wellbore Zone Treatment + Perforation type, but also for the Acidizing + Perforation and Optimization types, as determined by the same methodology. Statistical processing of all relevant criteria demonstrated that the analytical results obtained using the above equation provide an adequate description of the observed data [8].

Tables 4 to 7 provide a comparison between the calculated values of the efficiency indicators for the developed GTMs and their actual observed values, along with the corresponding deviations [8].

Table 3 – Values of the indicators calculated according to equation (10) for various GTMs

Table 4 – Absolute and relative deviations of the models for GTM (Near-Wellbore Zone Treatment + Perforation)

Forecasting Performance Metrics Across Diverse Assets Under Distinct Well Intervention Strategies and Subsequent Operational Choices

The analytical frameworks established earlier reflect how heavily formation properties dictate treatment outcomes. Consequently, these systems yield reliable performance projections for various enhanced oil recovery (EOR) techniques when applied to unfamiliar geological and geophysical environments. In other words, they provide the information necessary to forecast, compare, and optimally select the most effective EOR methods, including those previously applied in other fields.

Consequently, it becomes possible to conduct a comparative analysis of the outcomes for different GTM scenarios under various conditions. For this purpose, the constructed models were used to perform calculations for field conditions differing from those where the GTMs were originally applied. The results of these calculations are presented in Table 5.

Symbols not shown in Table 5, which represent geological requirements for the studied layers, are described in Table 1.

The performed calculations ensure that the most appropriate decision is made when selecting the EOR method that delivers the highest performance, based on actual field data. An engineering alternative achieves optimality strictly by satisfying every performance metric defined at the outset, meaning only these peak solutions enter further analysis [8]. These calculated benchmarks subsequently guided the selection of the most appropriate well intervention method for each studied scenario. The selection process relied on the five previously defined performance indicators as governing criteria, requiring unconditional compliance across all metrics.

Managing such multi-objective dilemmas—where a viable path must simultaneously clear every threshold—presents a notorious mathematical challenge. To navigate these complexities, modern research frequently leverages L. Zadeh’s fuzzy set framework. Under this paradigm, the ultimate decision is modeled as the confluence of target goals and operational constraints [11].

In accordance with the theory [12], eigenfunctions are evaluated for each GTM efficiency indicator. If the goal is to maximize an indicator, the corresponding eigenfunction is monotonically increasing and normalized from 0 to 1, where the maximum value of the indicator (both calculated and actual) approaches 1. If the indicator is minimized, the eigenvalue maps to the minimum value of that indicator.

Based on these observations, the eigenfunctions corresponding to each efficiency metric were defined using an exponential equation. Table 5 presents the values of the eigenfunctions alongside the efficiency indicators.

Table 1 contains the symbols describing the geological layers, while Table 5 contains the eigenfunction values for the decision set, which is equal to the minimum eigenfunction value among all GTM efficiency indicators for that scenario.

The optimal solution is identified as the one with the highest eigenvalue among the decision set. For each performance metric (μ₁–μ₅), the eigenvalues are calculated, and the smallest is recorded in the final column, representing the summary eigenfunction for the adopted decision. In this simplified definition, the fractional weights of all five efficiency indicators are assumed to be equal.

Furthermore, as noted in [13], it is important to consider partial weighting of indicators to ensure more responsible and accurate decision-making. When weighting factors are included, the resulting decision values are of higher quality. In this approach, the eigenfunction of the decision set is evaluated as follows [8]:

The largest values in the final column correspond to the recommended GTM for the oil layer indicated in that row. Selected compatibility indicators for the layer-GTM system are also presented in Table 5. These data, as well as the methodology, are thoroughly described in the author’s previous work [8].

For example, as shown in the table, the calculations identified “optimization” as the best measure for layer 6, even though the “Acidizing + Perforation” measure was implemented for that layer [8].

Table 5 – Eigenvalues and efficiency indicators

Conclusion

The findings of this study allow the following conclusions to be drawn:

A statistical analysis of oil production dynamics across various fields was conducted.

Forecasting based on statistical dependencies enabled a comparative assessment of oil production using both forecasted and actual data for specific years. The years for which forecasts were generated were characterized by the intensive implementation of geological and technical measures.

By converting the original formation properties into standalone latent factors, we derived mathematical functions that successfully map how these newly defined variables dictate treatment performance.

A method for selecting the optimal type of GTM was proposed, based on analysis of indicative calculations and the comparative efficiency of specific GTM types under various conditions.

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