A new approach to predict the formation pressure using multiple regression analysis: Case study from the Sukharev oil field reservoir – Russia

Ponomareva, Inna N.; Martyushev, Dmitriy A.; Govindarajan, Suresh

doi:10.1016/j.jksues.2022.03.005

Cited by 21 publications

(7 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Many researchers have recently been able to forecast the value of PP and fracture pressure using artificial intelligence algorithms in order to better predict the PP and FP in subsurface reservoirs. This is certainly relevant if the model is independent of the normal velocity trend and depends on the porosity (Rabbani and Babaei, 2019;Galkin et al, 2021;Ponomareva et al, 2021;Zakharov, 2021;Martyushev et al, 2022;Ponomareva et al, 2022). In 2000, Sadiq and Nashawi (2000) used artificial intelligence methods to predict formation failure pressure, which is the last point of formation PP.…”

Section: Literature Reviewmentioning

confidence: 99%

“…teaching-learning-based optimization algorithms (TLBO) (Choubineh et al, 2017;Ponomareva et al, 2022); firefly algorithm (FF) (Ghorbani et al, 2017c;Rao and Krishna, 2019;Zakharov et al, 2022); multilayer perceptron's (MLP), ANN and genetic optimization Cross plot for PP prediction by GS-GMDH based on two wells F 1 and F 2 data points (1511 data points).…”

Section: Recommendation For Future Workmentioning

confidence: 99%

See 1 more Smart Citation

Application of GMDH model to predict pore pressure

et al. 2023

View full text Add to dashboard Cite

Pore pressure (PP) is one of the essential and very critical parameters in the oil and gas industry, especially in reservoir engineering, exploitation, and production. Forecasting this valuable parameter can prevent huge costs incurred by the oil and gas industry. This research aims to develop a algorithm to better predict PP in subsurface -formations. Based on this, information from three wells (F1, F2, and F3) representing one of the Middle East oil fields was used in this research. The input variables used in this research include; laterolog (LLS), photoelectric index (PEF), compressional wave velocity (Vp), porosity (NPHI), gamma ray (spectral) (SGR), density (RHOB), gamma ray (corrected) (CGR), shear wave velocity (Vs), caliper (CALI), resistivity (ILD), and sonic transit time (DT). Based on the results presented in the heat map (Spearman’s correlation), it can be concluded that the pairs of parameters RHOB-PEF, CGR-SGR, RHOB-CALL, DT-PEF, PP-RHOB, Vs-RHOB, ILD-LLS, DT-CGR, and DT-NPHI are connected. In this research the GS-GMDH methods is used for modeling which is based on the Group method of data handling (GMDH). The results of this research show that this algorithm has an average error of RMSE = 1.88 Psi and R2 = 0.9997, indicating its high-performance accuracy. The difference between this method and the conventional GMDH method is that it can use three or more variables instead of two, which can improve prediction accuracy. Furthermore, by using the input of each neuron layer, the proposed model can communicate with other adjacent and non-adjacent layers to solve complex problems in the simplest possible way.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Recommendation For Future Workmentioning

confidence: 99%

Application of GMDH model to predict pore pressure

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Multiple regression is a statistical technique that can be used to analyse the relationship between a single dependent variable and several independent variables. The objective of multiple regression analysis is to use an independent variable whose value is known to predict the value of a single dependent variable (Ponomareva, Martyushev & Govindarajan, 2022).…”

Section: Multiple Regression Analysismentioning

confidence: 99%

Factors Influencing Financial Literacy: Evidence From Economics And Financial Undergraduate Students

Mohd Radzi,

Haron,

Saidi

et al. 2023

IJAREMS

View full text Add to dashboard Cite

Financial literacy can have a significant influence on individuals' lives. Faced with the challenge of adapting to the new norms post-pandemic, there are numerous aspects that need to be prioritised in daily life, particularly in terms of finances. The significance of financial literacy is well recognised, as insufficient financial literacy among young adults, particularly students, can result in financial hardships. According to the literature, inadequate financial management practices are indicative of young individuals falling into the cycle of financial difficulties. Therefore, this study aims to identify the extent to which financial knowledge and financial behaviour influence the level of financial literacy among undergraduate students in the Department of Economics and Financial Studies. The research methodology in the present study employs a quantitative approach by administering a survey to undergraduate students. The data was subsequently analysed using SPSS version 29 for descriptive and regression analysis. This study's findings indicate that financial knowledge is the most influential factor in the level of financial literacy among undergraduate students. It is recommended for future researchers or studies to incorporate additional variables that can exert a more substantial influence on financial literacy.

show abstract

“…For the well cleanup process where the liquid gas ratio changes dramatically, it is not easy to obtain accurate wellbore temperature and pressure change rules, and it is difficult to describe the distribution and change characteristics of gas–liquid flow patterns. To solve above problems, on the basis of analyzing the cleanup conditions of deep-water gas wells, the transient multiphase flow simulation software OLGA was innovatively used for the first time to carry out the transient numerical simulation of well cleaning, − which revealed the characteristics of wellbore gas–liquid flow during the well cleanup stage. At the same time, a hydrate prediction model was established for the well cleaning process, which predicted the hydrate formation risk in the wellbore under different well cleanup conditions, it provides theoretical guidance for the research on deep-water wellbore flow assurance.…”

Section: Introductionmentioning

confidence: 99%

Gas–Liquid Flow Pattern and Hydrate Risk in Wellbore during the Deep-Water Gas-Well Cleanup Process

Chen

Sun

2023

ACS Omega

View full text Add to dashboard Cite

The wellbore fluid flow characteristics and hydrate flow assurance problems during the deep-water gas well cleanup process seriously affect the safety of gas well testing. Aiming at the process of deep-water gas well cleanup where the liquid gas ratio changes dramatically, this study reveals the distribution law of gas−liquid flow patterns in the wellbore. Combined with the clean test conditions of deep-water gas wells, the transient numerical simulation and analysis are carried out. At the same time, a hydrate risk prediction model suitable for the well cleanup process is established to predict the hydrate risk under different cleaning conditions. The research results are as follows: (1) after flowback, the temperature at the wellhead and mud line presents different characteristics of temperature change, the wellhead temperature decreases first and then increases, while the flow temperature at the mud line rises first and then decreases; (2) slug flow and annular flow are more likely to occur in the wellbore under low wellhead pressure. Bubble flow often appears in the initial stage of flowback under high wellhead pressure and gradually changes into slug and annular flow patterns in the middle and late stages; (3) at the same flowback time, the wellbore will also present different flow patterns along the way. The lower part of the well bore mainly presents bubble flow and slug flow, and the upper and middle parts of the well bore mainly present annular flow patterns; (4) high liquid−gas ratio conditions, proper well cleaning speed, and the use of hydrate inhibitors can effectively reduce the hydrate risk during the cleaning process of deep-water gas wells.

show abstract

A new approach to predict the formation pressure using multiple regression analysis: Case study from the Sukharev oil field reservoir – Russia

Cited by 21 publications

References 14 publications

Application of GMDH model to predict pore pressure

Application of GMDH model to predict pore pressure

Factors Influencing Financial Literacy: Evidence From Economics And Financial Undergraduate Students

Gas–Liquid Flow Pattern and Hydrate Risk in Wellbore during the Deep-Water Gas-Well Cleanup Process

Contact Info

Product

Resources

About