Production Forecasts Research Articles

Multiphase friction calculation peculiarities based on models implemented in the "d-Flow" software package

In order to ensure efficient and safe operation of oil and gas fields, a tool is required to design production wells and surface infrastructure. It is important to take into account the physical processes that occur during production, as well as fluid properties and phase transitions. The "d-Flow" software package enables the creation of a comprehensive field model based on geological and field data to calculate hydraulic losses of pipelines and forecast hydrocarbon production. The fluid flow modelling is based on the calculation of multiphase friction with the wall of a well or a pipeline. The article discusses the implementation of four friction models: the Beggs-Brill model, the Gray model and its modification, and the Mukherjee-Brill model. The purpose of this work is to compare the predictions of friction models with the results obtained using commercial realizations of the same models. The results of the Schlumberger PIPESIM hydraulic simulator calculations were used as a benchmark for comparison with the "d-Flow" models. Numerical experiments were conducted to investigate two-phase flow under varying well geometry and flow regimes. The models used were compared, and the results showed that the predicted liquid holdup had an average relative error of 0.06%. In some cases, the error was as low as 0.02%. The predicted cumulative pressure drop in the well did not exceed 0.34% for all considered models. Based on the comparison results, we conclude that the "d-Flow" software package is suitable for calculating pressure drop in wells of different geometry and surface networks.

Analytical Method for Production Forecasting During Boundary-Dominated Flow in Unconventional Volatile Oil Reservoirs Using Single-Phase Analogy

Abstract This paper presents an analytical methodology for production forecasting of wells exhibiting boundary-dominated flow in unconventional volatile oil reservoirs. An analytical model using single-phase analogy is established to predict the production rate of a multi-fractured horizontal well under multiphase flow. Pseudo-variables are employed to linearize governing differential flow equations. Therefore, the analytical model for efficiently handling multiphase flow is essentially an advanced adaptation of existing single-phase models, in which we derive new multiphase fluid properties. To calculate the pseudo-variables accurately, we summarize accessible methods for the determination of saturation–pressure (S–P) relation and further provide an appropriate way to calculate multiphase pseudo-variables in unconventional volatile oil reservoirs based on simulation results. The simulation study indicates that the S–P relation given by constant volume depletion data for near-critical volatile oils leads to satisfactory results, and the S–P relation derived from the tank-type model is appropriate for ordinary volatile oils. The analytical solution and associated methods were validated through comparison with results from a compositional simulator; the excellent agreement during boundary-dominated flow demonstrated the accuracy of the analytical methodology. The analytical methodology can greatly reduce computation and is justified to make production forecasting in unconventional volatile oil reservoirs, this tractable methodology should be attractive to the industry.

Spatiotemporal wave forecast with transformer-based network: A case study for the northwestern Pacific Ocean

The forecast of ocean waves relies mostly on complex dynamic-based models, which are expensive in computation and demanding in professional skills to run. Diverse deep learning methods have been proposed to tackle this problem, yet the architecture of Transformer (i.e., the self-attention) was seldom tested for such a learning problem. To bridge this gap, we apply a state-of-the-art spatiotemporal attention network, the EarthFormer, and apply it for wave forecasting in the northwestern Pacific. To train and validate the EarthFormer, the fifth-generation atmospheric reanalysis dataset (ERA5) from the European Centre for Medium-Range Weather Forecast product with hourly resolution was adopted, with the sequence of wind as the input and the three key variables (i.e., significance wave height, mean wave period, and mean wave direction) of ocean waves as the output. The prediction can generate accurate wave forecasts up to 12 h ahead, with considerably low root-mean-squared error (RMSE). Overall, the predicted waves resulted in RMSE for the significance wave height of 0.22 m, for the mean wave period of 0.68 s, and for the mean wave direction of 0.28 rad. These wave height and wave period values are ∼13 % and ∼10 % of corresponding spatiotemporal mean values. The EarthFormer outperformed a popular spatiotemporal forecast network, the ConvLSTM, in our problem. For site-wise forecasts against buoy observations near Taiwan, the EarthFormer forecast also presents considerably high accuracy comparable to the ERA5 forecasts. This method can therefore provide an accurate and prompt way to forecast the large-scale distribution of waves to better model the marine dynamic and its climate effects, which could have a high potential for disaster prevention and climate modeling.

Bayesian variable pressure decline-curve analysis for shale gas wells

Decline-curve analysis and production forecasting are usually performed from a deterministic standpoint (point estimation). This approach does not quantify the uncertainty of the model's parameters and thus, the model's estimated ultimate recovery. In addition, decline-curve models do not consider the variations in the bottomhole flowing pressure, which can greatly impact the accuracy of the model's predictions. This work combines a new technique that incorporates variable bottomhole flowing pressure conditions into decline-curve models with Bayesian inference to improve the accuracy of production history-matches while quantifying the uncertainty of the model's parameters and its future production prediction. The method provides fast production history-matches and forecasts of shale gas wells (taking around 1 min per well) and it is more accurate than traditional decline-curve analysis for wells subject to variable bottomhole flowing pressure conditions while quantifying the uncertainty in the model's parameters and estimated ultimate recovery. The main contribution of this work is the illustration of a new method for probabilistic variable pressure decline-curve analysis. We present the application of this workflow for shale gas wells.

Методи прийняття багатоцільових рішень щодо управління транспарентністю розвитку підприємств в умовах конвергенції інвестиційних процесів

It is determined that one of the priorities in any national economy is the problem of forecasting and assessing the level of transparency of development, quality and reliability of functioning of industrial enterprises, their technological and economic subsystems. This has a direct impact on the level of investment, the nature of financing, the distribution of costs for the production of finished products with the achievement of a given quality and reliability, as well as on the financial condition and image. In addition, it has a direct proportional effect on the well-being of society and the quality of life of the population. On the other hand, investing in the real sector of the economy requires banks and other investors not only to optimize investment programs, but also to take into account the specifics of financial, production, economic and socioeconomic activities of industrial enterprises as market participants, and, accordingly, application of models that appropriately link investments with sales policies, production, management, staff, location of production facilities, taxation, etc. It is concluded that the positive level of management of transparency of development of industrial enterprises in the context of convergence of investment processes is inseparable from the processes of planning, regulation, control and forecasting of production and technological processes, as well as the allocation of resources in the process of performing certain complex tasks. Determining the optimal options for current and future development is often associated with solving large-dimensional optimization problems with a large number of different conditions and constraints (for example, requirements for the completeness of variables combined with requirements for the absence of time losses), which, due to their extreme nature, complicate the solution of such problems. Effective methods for optimizing multi-purpose investment decisions in managing the transparency of the development of industrial enterprises in the context of convergence of investment processes include methods for analyzing the usefulness of goals, criteria and alternatives, including those with several vectors; methods of hierarchy of goals, taking into account a number of advantages; information about the chosen strategy, alternatives, their parameterization, stochasticity, substitution ratios, and other characteristics. All these factors can be taken into account for the classification of models of multi-purpose and program-target solutions and the use of the most effective of them when choosing the optimal investment solution in managing the transparency of the development of industrial enterprises in the context of convergence of investment processes.

A Simple Normalized Analytical Model for Oil Production of SAGD Process and Its Applications in Athabasca Oil Sands

Summary Since the late 1980s, when the Alberta Oil Sands Technology and Research Authority Underground Test Facility project first demonstrated the feasibility of the steam-assisted gravity drainage (SAGD) technology, many commercial SAGD projects were brought online in Western Canada. Now, many of these projects have late-life SAGD wells approaching their ultimate SAGD recovery factors. Although these projects have demonstrated highly variable production performance, there is an opportunity to use the industry production data to find what they have in common and develop a normalized SAGD model. For this paper, we collected oil production history from several leading SAGD projects with late-life production in the Athabasca oil sands area and confirmed the three stages in an SAGD project lifespan: chamber rising, chamber spreading, and chamber falling stages. By normalizing the field data, all SAGD projects converged to one type curve, regardless of reservoir quality and operating conditions. Based on this observation, a new simple normalized model is derived to model the bitumen production in a typical SAGD process for Athabasca oil sands. The new model bridges the gap between the existing SAGD analytical model and conventional decline analysis and provides oil production forecasts based on the inputs for the five-component recovery factor method defined in the Canadian Oil and Gas Evaluation Handbook(Society of Petroleum Evaluation Engineers 2018). The model has been applied to one of the thermal projects to history match the field production. By running a Monte Carlo simulation, this model further demonstrates its capability to capture the uncertainty of the production forecast for the project at different stages of SAGD operation. In addition, by properly modifying the type curve of the analytical model, a similar workflow can be used to model cases with special reservoir quality or different operational limitations.

Map-Based Ensemble Forecasts for Maritime Operations: An Interactive Usability Assessment with Decision Scenarios

Abstract Previous research indicates that forecast uncertainty can, in certain formats and decision contexts, provide actionable insights that help users in their decision-making. However, how to best disseminate forecast uncertainty, which factors affect successful uptake, and how forecast uncertainty transforms into better decision-making remains an ongoing topic for discussion in both academic and operational contexts. Interpreting and using visualizations of forecast uncertainty are not straightforward, and choosing how to represent uncertainty in forecast products should be dependent on the specific audience in mind. We present findings from an interactive stakeholder workshop that aimed to advance context-based insights on the usability of graphical representations of forecast uncertainty in the field of maritime operations. The workshop involved participants from various maritime sectors, including cruise tourism, fisheries, government, private forecast service providers, and research/academia. Geographically situated in Norway, the workshop employed sea spray icing as a use case for various decision scenario exercises, using both fixed probability and fixed threshold formats, supplemented with temporal ensemble diagrams. Accumulated operational expertise and characteristics of the forecast information itself, such as color coding and different forms of forecast uncertainty visualizations, were found to affect perceptions of decision-making quality. Findings can inform codesign processes of translating ensemble forecasts into usable and useful public and commercial forecast information services. The collaborative nature of the workshop facilitated knowledge sharing and coproduction between forecast providers and users. Overall, the study highlights the importance of incorporating methodological approaches that consider the complex and dynamic operational contexts of ensemble-based forecast provision, communication, and use. Significance Statement We wanted to understand how maps showing uncertainty in weather forecasts can help maritime users in their operational decisions. We organized a workshop with Norwegian maritime stakeholders and forecasters, who interpreted maps that combined layers of maritime operational activities and the likelihood of sea spray icing (an important hazard for ships operating on higher latitudes). The results show that contextual knowledge, and the use visual formats such as traffic light colors may help users to understand the maps. The results will help to better communicate weather forecasts to maritime users and gives suggestions about how to involve users in codesigning forecast products. Follow-up research could use our approach to investigate other hazardous conditions, such as wind, waves and sea ice.

Study and application on the optimal quantile forecast of precipitation in an ensemble forecast system

AbstractQuantiles of precipitation are widely used in ensemble forecast systems. At present, the common practice is to provide precipitation amounts corresponding to different quantiles to users directly, which will make it difficult for users to extract reliable forecast information. Therefore, this study investigates the statistically optimal (using threat score (TS) as a metric) quantiles of precipitation in an ensemble forecast system constructed using the WRF V4.0 model. The main conclusions are as follows: The threat‐score‐optimal quantiles for light rain, moderate rain, heavy rain, rainstorm, and heavy rainstorm forecasts are 40%–60%, 60%–70%, 60%–80%, 70%–80%, and 80%, respectively. Overall, the optimal quantile increases with the rise in precipitation magnitude or the extension of forecast lead time. All the optimal quantile forecast products have higher TS than the corresponding control forecast, ensemble mean forecast, and probability‐matched ensemble mean forecast products. The merged threat‐score‐optimal quantile forecast product formed by combining the optimal quantile forecasts of different precipitation magnitudes shows obvious advantages over other products in statistical verification and case studies, and it shows good potential to be operationally implemented in the future.

Прогнозирование динамики ВВП на основе данных мониторинга предприятий, проводимого Банком России

The article is devoted to modeling and forecasting of the gross domestic product of Russia based on the data of enterprise survey conducted by the Bank of Russia. The purpose was to study the possibility of improving the efficiency and accuracy of GDP forecasting based on the use of these data. The authors used business climate indicators as well as balances of responses to questions about dynamics of output, demand, and prices. The methodology included regression analysis, as well as a combination of factor and regression analysis, both on aggregate data on all economic sectors and on data on individual economic branches. The forecasting results for two control samples containing actual observations for the eight quarters of 2017–2018 and the five quarters of 2022–2023 suggested that the use of monitoring data improved forecasting accuracy compared to the ARIMA benchmark model. The lowest estimation errors were obtained for approaches combining factor and regression analysis on aggregate data for all economic sectors or the economy as a whole.

Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems

Climate change demands clean energy solutions, and renewable sources such as solar and wind are prime candidates. However, their variability poses challenges for their integration into large-scale power systems. This paper addresses this issue by proposing a novel hybrid mathematical model. The proposal integrates both fossil and renewable sources, considering real-world constraints such as system demand, reserves, and transmission dynamics. The model combines several approaches. By using a novel block composition technique, the computational complexity is reduced, making the model applicable to large-scale systems. A neural network is also developed to improve the forecasting of renewable energy production, which is crucial for managing its intermittency. The effectiveness of the proposed model is tested by considering the large Argentinean electricity system, demonstrating its practical applicability. The results show that acceptable forecasts can be obtained for the generation and transmission scheduling of the whole system.

СОВЕРШЕНСТВОВАНИЕ ПРОГНОЗИРОВАНИЯ СОЦИАЛЬНО-ЭКОНОМИЧЕСКОГО РАЗВИТИЯ РЕГИОНА

The article presents an algorithm for forecasting the socio-economic development of the region based on artificial intelligence models, a methodological approach and tools for forecasting the gross regional product, adaptive forecasts of the gross regional product of the Altai Territory, recommendations for the use of adaptive forecasts in strategic planning of the region. The purpose of the study is to develop an algorithm, a methodological approach and tools for forecasting the socio-economic development of the region based on artificial intelligence models. The study is based on official data from the Federal State Statistics Service. The main stages of forecasting the socio-economic development of the region based on artificial intelligence models are: identification of factors that have a dominant influence on the dynamics of forecast indicators, development of a methodological approach to forecasting based on the construction and use of artificial intelligence models, development of models for forecasting. The results of the conducted research provide grounds for the development and correction of strategic planning documents of the Altai Territory.

ВИКОРИСТАННЯ ФУНКЦІЇ КОББА-ДУГЛАСА ДЛЯ АНАЛІЗУ ЕКОНОМІЧНОЇ ДИНАМІКИ

The purpose of the study is to enhance the capabilities of analyzing the dynamics and efficiency of the national economy, as well as forecasting its development, based on the use of the Cobb-Douglas production function apparatus. To this end, the article demonstrates that production functions, including the Cobb-Douglas function, are powerful tool for analyzing economic dynamics. The advantages of the Cobb-Douglas function and its application peculiarities are analyzed. Production functions enable the evaluation of production efficiency and the productivity of factors such as labor, capital, etc. They can be used to forecast trends in the national economy's development. However, using the Cobb-Douglas production function can cause some problems and limitations. The article presents four calculation options based on actual statistical data of the Ukrainian economy. The analysis of the calculations revealed that it is advisable to use the following indicators to obtain accurate results. The dependent output variable is gross domestic product at actual prices. The independent variables are the cost of capital investment at actual prices and the effective volume of labor. Based on the calculations, the parameters of the Cobb-Douglas production function for the Ukrainian economy were found. The adequacy of the constructed econometric model and the feasibility of its application for analyzing the dynamics of the national economy of Ukraine have been demonstrated. 93.6% of the variation in the gross domestic product depends on the variation in the effective volume of labor and the variation in capital investment. The economic interpretation of the parameters of the constructed Cobb-Douglas production function is provided. It was concluded that labor-intensive economic growth is occurring in the economy of Ukraine, along with a certain tendency towards intensive economic growth. The constructed Cobb-Douglas model was applied to find a point forecast of the gross domestic product. The values of capital return and labor productivity for the economy of Ukraine were determined. The results of the work can be used to analyze the economic dynamics of indicators of the national economy.

Forecasting of Fish Production of India using ARIMA Models

Fish is an essential component of the diet of the most popular in India. Fish production in India has increased at a higher rate compared to other food items. The most widely used time series models ARIMA was applied for modelling and forecasting of total, inland and marine fish production in India for a period from 1978-79 to 2021-22. The collected data during the third decade was found highly consistent and stable with less variability. The lowest mean fish production growth rate was observed in all three categories viz., Inland, Marine and Total fish production, in the fourth decade of study period, i.e., 2003-2012. Present study revealed that the model ARIMA (0,1,3) was best fit for forecasting marine fish production of India while ARIMA (0,2,1) was best model for both inland and overall fish production. The model forecasted that the fish production in all three categories would be in increasing trend for the next five years from 2022-23 to 2026-27 with highest estimated marine, inland and total fish production of 42.18 lakh tonne 162.78 lakh tonne and 211.64 lakh tonnes respectively during the year 2026-27.

Agent-based Option for Solving Problems of Oil Production Digital Forecasting

The existing problems in the use of computer forecasting of oil production and effectiveness of well stimulation measures, associated primarily with the high uncertainty of functional dependencies, presence of many heterogeneous objects and limited amount of available geological and field information, are discussed. As an alternative option for geological and hydrodynamic modeling of petroleum reservoirs, an agent-based approach is proposed, characterized in that instead of traditional partial differential equations or artificial neural networks, agent-based geological and hydrodynamic models are used to predict oil production and effectiveness of well stimulation measures. The models are calculated according to some local rules that do not contradict with the global laws of underground hydraulics, and using cascades of fuzzy-logic matrices, each of which contains about a thousand of different parameters. Thus, it is possible to create a mathematical tool close to the “strong” artificial intelligence, capable of independent decision-making and realistic forecasting.

Modeling and Forecasting of Coconut Area, Production, and Productivity Using a Time Series Model

The study aimed to compare ARIMA and Holt's models for predicting coconut metrics in Kerala. The coconut data series was collected from the period 1957 to 2019. Of this, 80% of the data (from 1957 to 2007) is treated as training data, and the rest (20% from 2008 to 2019) is treated as testing data. Ideal models were selected based on lower AIC and BIC values. Their accuracy was evaluated through error estimation on testing data, revealing Holt's exponential, linear, and ARIMA (0,1,0) models as the bestfit choices for predicting coconut area, production, and productivity respectively. After using the testing data, we tried for the forecasting for 2020-2024 using these models, and the DM test confirmed their significant forecasting accuracy. This comprehensive analysis provides valuable insights into effective prediction models for coconut-related metrics, offering a foundation for informed decision-making and future projections.

Forecast of economic gas production in the Marcellus Shale

Forecast of economic gas production in the Marcellus Shale

Аналіз та огляд прогнозів світового виробництва металургійного коксу

DOI: 10.31081/1681-309X-2024-0-1-3-7 Specialty 161. U.D.C. 338.27:662.749.2 ANALYSIS AND REVIEW OF FORECASTS OF WORLD METALLURGICAL COKE PRODUCTION © S.A. Kravchenko, PhD in Technical Sciences, S.V. Chaplianko, PhD in Technical Sciences "STATE INSTITUTE FOR DESIGNING ENTERPRISES OF COKE OVEN AND BY-PRODUCT PLANTS'' (SE "GIPROKOKS''), 61002, 60 Sumska str., Kharkiv, Ukraine) The article considers and analyses the volumes of global iron and steel production in 2019-2023. It is shown that during this period it had fluctuating trends: the deviation between the minimum and maximum values was 109 million tonnes (7.7 %) for iron and 112 million tonnes (5.7 %) for steel. Domestic steel production in 2019-2021 was characterised by an increase of 1.1 million tonnes (5.2 %) in iron and fluctuations (the deviation was 0.8 million tonnes, i.e. 3.7 %) in steel, and a sharp decrease of 14.8 million tonnes (69.7 %) in steel. t (69.8 %) for iron and 15.1 million tonnes (70.6 %) for steel in 2022 and a further decrease of 0.4 million tonnes (6.2 %) for iron and 0.1 million tonnes (1.6 %) for steel in 2023 is due to the consequences of Russia's armed aggression against Ukraine. The article considers and analyses the volumes of global production of metallurgical coke in 20192021. It is shown that during this period it had fluctuating trends: the deviation was 21 million tonnes (3.0 %). The volume of coke production in China in 2020-2023 increased at an average annual growth rate of 2.2, 2.4, and 3.8 %, respectively. Domestic production of metallurgical coke in 20192021 was characterised by a decrease in volume by 0.44 million tonnes (5.2 %), and a rapid decrease by 5.6 million tonnes (59.0 %) in 2022 is also due to the consequences of armed aggression. Separate forecasts for global metallurgical coke production in the short and long term are considered. It is shown that there is no consensus on the development of global coke production. The most negative scenario is a decline in global coke production to $84.016 billion (USD) in 2029, while the most positive is an increase to $325.78 billion in 2034. In 2024, on average, the global metallurgical coke market will be around $106 billion according to negative forecasts and around $205 billion according to positive forecasts. Given the World steel Association's weak positive outlook for global steel production, as well as the existing increase in coke production in China in 2020-2023 and the planned expansion of coke production capacity in India and Brazil, a restrained positive outlook for global coke production is most likely (for 2024 and beyond). Keywords: cast iron, steel, metallurgical coke, coke-chemical production, analysis, forecast. Corresponding author S.V. Chaplyanko, e-mail: chaplianko_sv@ukr.net

Demand Forecasting Analysis of Body Scrub Product at PT. XYZ Using Autoregressive Integrated Moving Average (ARIMA) Method

PT. XYZ is one of the private companies engaged in the cosmetics industry, especially as a manufacturer of SPA products with natural raw materials. The SPA products produced by PT. XYZ are various kinds of scrubs, body scrubs, masks, essential oils, soaps and other products. Body scrub is the most popular product or best seller in this company. The purpose of this study is to find the best ARIMA model in calculating demand forecasting of body scrub products at PT. XYZ for the period January to December 2024. The method used in this research is the Autoregressive Moving Average (ARIMA) method. There are two ARIMA models that can be used in this study, namely ARIMA (1,1,0) and ARIMA (0,1,1). The selected ARIMA model used to calculate body scrub forecasting at PT. XYZ is the ARIMA (1,1,0) model because it has a smaller error value, namely MSE = 745.46 and MAPE = 7.40%. The results of forecasting the demand for body scrub products at PT. XYZ for the period January to December 2024 using the ARIMA (1,1,0) model show that demand has increased every month with an average demand of 343.4 kg. The error obtained by comparing the forecasting results with the actual sales data for 6 months, namely January - June 2024 is 2.45%, which means that the accuracy of forecasting in 6 months reaches 97.55%.

Analysing Forecasts of Sustainable Agri-Food Consumption, Production and Hunger

Analysing Forecasts of Sustainable Agri-Food Consumption, Production and Hunger

КОНКУРЕНТОСПРОМОЖНІСТЬ ПІДПРИЄМСТВА ТА ФАКТОРИ, ЩО ЇЇ ФОРМУЮТЬ

The article investigates and substantiates the theoretical aspects of the essence of competitiveness and the study of internal and external environment factors that affect the competitiveness of an enterprise in market conditions. On the basis of theoretical studies, the author summarizes some of the author's definitions of the concept of “enterprise competitiveness”. It has been determined that the competitiveness of enterprise is characterized by its ability and efficiency to adapt to the conditions of the competitive environment. The features that characterize the competitiveness of an enterprise include: comparability, spatiality, dynamism, objectivity, attributability, systematicity, objectivity. Observance of these characteristics is a prerequisite for conducting analytical research, a key to solving problems and achieving the company's goals. It is proposed to use the modified classification of factors of competitiveness of a firm proposed by O. Kuzmin, which provides for the allocation of the following criterion (integral) groups of factors: environment of the firm's functioning; substantive characteristics; level of regulation of factors. The environmental factors are reasonably divided into direct factors that have a direct impact on competitiveness and indirect factors that have an indirect impact through direct factors. An analysis of the impact of external and internal factors on the company's competitiveness helps to reduce uncertainty and risks in the company's production and sales process, improve the quality of strategic planning and forecasting of the company's production and sales activities, increase the competitiveness of the company and its products, maintain the company's competitive position and increase its market share. The importance of certain substantive factors of competitiveness may vary depending on a number of objective factors, such as the purchasing power of the population, changes in regulation and taxation, market conditions, etc.