Observed Variations in Hydrocarbon Reservoir Bacterial Populations with Temperature: A First Step in Modeling the Bacterial Populations of Hydrocarbon Reservoirs.

1999 ◽  
Author(s):  
Jonathan M. Wood ◽  
Iain S.C. Spark
Keyword(s):  
Hydrocarbon Reservoirs ◽  
Bacterial Populations ◽  
Hydrocarbon Reservoir

scholarly journals From axiomatics of quantum probability to modelling geological uncertainty and management of intelligent hydrocarbon reservoirs with the theory of open quantum systems

2018 ◽  
Vol 376 (2118) ◽  
pp. 20170225 ◽  
Author(s):  
Miguel Ángel Lozada Aguilar ◽  
Andrei Khrennikov ◽  
Klaudia Oleschko
Keyword(s):  
Decision Making ◽  
Hilbert Problem ◽  
Open Quantum Systems ◽  
Quantum Probability ◽  
Quantum Systems ◽  
Belief State ◽  
Hydrocarbon Reservoirs ◽  
Continue Development ◽  
Open Quantum ◽  
Hydrocarbon Reservoir

As was recently shown by the authors, quantum probability theory can be used for the modelling of the process of decision-making (e.g. probabilistic risk analysis) for macroscopic geophysical structures such as hydrocarbon reservoirs. This approach can be considered as a geophysical realization of Hilbert's programme on axiomatization of statistical models in physics (the famous sixth Hilbert problem). In this conceptual paper , we continue development of this approach to decision-making under uncertainty which is generated by complexity, variability, heterogeneity, anisotropy, as well as the restrictions to accessibility of subsurface structures. The belief state of a geological expert about the potential of exploring a hydrocarbon reservoir is continuously updated by outputs of measurements, and selection of mathematical models and scales of numerical simulation. These outputs can be treated as signals from the information environment E . The dynamics of the belief state can be modelled with the aid of the theory of open quantum systems: a quantum state (representing uncertainty in beliefs) is dynamically modified through coupling with E ; stabilization to a steady state determines a decision strategy. In this paper, the process of decision-making about hydrocarbon reservoirs (e.g. ‘explore or not?'; ‘open new well or not?’; ‘contaminated by water or not?’; ‘double or triple porosity medium?’) is modelled by using the Gorini–Kossakowski–Sudarshan–Lindblad equation. In our model, this equation describes the evolution of experts' predictions about a geophysical structure. We proceed with the information approach to quantum theory and the subjective interpretation of quantum probabilities (due to quantum Bayesianism). This article is part of the theme issue ‘Hilbert's sixth problem’.

A Combined Wavelet Transform and Recurrent Neural Networks Scheme for Identification of Hydrocarbon Reservoir Systems From Well Testing Signals

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Mehrafarin Moghimihanjani ◽  
Behzad Vaferi
Keyword(s):  
Neural Networks ◽  
Wavelet Transform ◽  
Recurrent Neural Networks ◽  
Predictive Accuracy ◽  
Hydrocarbon Reservoirs ◽  
Real Field ◽  
Well Testing ◽  
Hydrocarbon Reservoir ◽  
Reservoir Systems

Abstract Oil and gas are likely the most important sources for producing heat and energy in both domestic and industrial applications. Hydrocarbon reservoirs that contain these fuels are required to be characterized to exploit the maximum amount of their fluids. Well testing analysis is a valuable tool for the characterization of hydrocarbon reservoirs. Handling and analysis of long-term and noise-contaminated well testing signals using the traditional methods is a challenging task. Therefore, in this study, a novel paradigm that combines wavelet transform (WT) and recurrent neural networks (RNN) is proposed for analyzing the long-term well testing signals. The WT not only reduces the dimension of the pressure derivative (PD) signals during feature extraction but it efficiently removes noisy data. The RNN identifies reservoir type and its boundary condition from the extracted features by WT. Results confirmed that the five-level decomposition of the PD signals by the Bior 1.1 filter provides the best features for classification. A two-layer RNN model with nine hidden neurons correctly detects 3202 out of 3298 hydrocarbon reservoir systems. Performance of the proposed approach is checked using smooth, noisy, and real field well testing signals. Moreover, a comparison is done among predictive accuracy of WT-RNN, traditional RNN, conventional multilayer perceptron (MLP) neural networks, and couple WT-MLP approaches. The results confirm that the coupled WT-RNN paradigm is superior to the other considered smart machines.

A general finite volume based numerical algorithm for hydrocarbon reservoir simulation using blackoil model

2014 ◽  
Vol 24 (8) ◽  
pp. 1831-1863 ◽  
Author(s):  
Mehdi Mosharaf Dehkordi ◽  
Mehrdad T. Manzari ◽  
H. Ghafouri ◽  
R. Fatehi
Keyword(s):  
Finite Volume ◽  
Numerical Algorithm ◽  
Numerical Results ◽  
Hydrocarbon Reservoirs ◽  
Pressure Equation ◽  
Content Type ◽  
Structured Grids ◽  
Hydrocarbon Reservoir ◽  
Computational Performance ◽  
Wide Range

Purpose – The purpose of this paper is to present a detailed algorithm for simulating three-dimensional hydrocarbon reservoirs using the blackoil model. Design/methodology/approach – The numerical algorithm uses a cell-centred structured grid finite volume method. The blackoil formulation is written in a way that an Implicit Pressure Explicit Saturation approach can be used. The flow field is obtained by solving a general gas pressure equation derived by manipulating the governing equations. All possible variations of the pressure equation coefficients are given for different reservoir conditions. Key computational details including treatment of non-linear terms, expansion of accumulation terms, transitions from under-saturated to saturated states and vice versa, high gas injection rates, evolution of gas in the oil production wells and adaptive time-stepping procedures are elaborated. Findings – It was shown that using a proper linearization method, less computational difficulties occur especially when free gas is released with high rates. The computational performance of the proposed algorithm is assessed by solving the first SPE comparative study problem with both constant and variable bubble point conditions. Research limitations/implications – While discretization is performed and implemented for unstructured grids, the numerical results are presented only for structured grids, as expected, the accuracy of numerical results are best for structured grids. Also, the reservoir is assumed to be non-fractured. Practical implications – The proposed algorithm can be efficiently used for simulating a wide range of practical problems wherever blackoil model is applicable. Originality/value – A complete and detailed description of ingredients of an efficient finite volume-based algorithm for simulating blackoil flows in hydrocarbon reservoirs is presented.

scholarly journals Self-potential anomalies induced by water injection into hydrocarbon reservoirs

Geophysics ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. F283-F292 ◽  
Author(s):  
Murtaza Y. Gulamali ◽  
Eli Leinov ◽  
Matthew D. Jackson
Keyword(s):  
Cold Water ◽  
Water Injection ◽  
Hydrocarbon Reservoirs ◽  
Coupling Coefficients ◽  
Production Well ◽  
Thermoelectric Effects ◽  
Hydrocarbon Reservoir ◽  
Partially Saturated Porous Media ◽  
Self Potential ◽  
Saturation Front

The injection of cold water into a hydrocarbon reservoir containing relatively warmer, more saline formation brine may generate self-potential anomalies as a result of electrokinetic, thermoelectric, and/or electrochemical effects. We have numerically assessed the relative contributions of these effects to the overall self-potential signal generated during oil production in a simple hydrocarbon reservoir model. Our aim was to determine if measurements of self-potential at a production well can be used to detect the movement of water toward the well. The coupling coefficients for the electrochemical and thermoelectric potentials are uncertain, so we considered four different models for them. We also investigated the effect of altering the salinities of the formation and injected brines. We found that the electrokinetic potential peaked at the location of the saturation front (reaching values of 0.2 mV even for the most saline brine considered). Moreover, the value at the production well increased as the front approached the well, exceeding the noise level (∼ 0.1 mV). Thermoelectric effects gave rise to larger potentials in the reservoir (∼10 mV), but values at the well were negligible [Formula: see text] until after water breakthrough because of the lag in the temperature front relative to the saturation front. Electrochemical potentials were smaller in magnitude than thermoelectric potentials in the reservoir but were measurable [Formula: see text] at the well because the salinity front was closely associated with the saturation front. When the formation brine was less saline (∼1 mol/liter), electrokinetic effects dominated; at higher salinities (∼5 mol/liter), electrochemical effects were significant. We concluded that the measurement of self-potential signals in a production well may be used to monitor the movement of water in hydrocarbon reservoirs during production, but further research is required to understand the thermoelectric and electrochemical coupling coefficients in partially saturated porous media.

Large Scale Placement For Multilateral Wells Using Network Optimization

2021 ◽  
Author(s):  
Ghazi D. AL-Qahtani ◽  
Noah Berlow
Keyword(s):  
Network Optimization ◽  
Large Scale ◽  
Computational Cost ◽  
Hydrocarbon Reservoirs ◽  
Reservoir Rock ◽  
Optimization Approach ◽  
Hydrocarbon Reservoir ◽  
Novel Technology ◽  
New Generation ◽  
Different Levels

Abstract Multilateral wells are an evolution of horizontal wells in which several wellbore branches radiate from the main borehole. In the last two decades, multilateral wells have been increasingly utilized in producing hydrocarbon reservoirs. The main advantage of using such technology against conventional and single-bore wells comes from the additional access to reservoir rock by maximizing the reservoir contact with fewer resources. Today, multilateral wells are rapidly becoming more complex in both designs and architecture (i.e., extended reach wells, maximum reservoir contact, and extreme reservoir contact wells). Certain multilateral design templates prevail in the industry, such as fork and fishbone types, which tend to be populated throughout the reservoir of interest with no significant changes to the original architecture and, therefore, may not fully realize the reservoir's potential. Placement of optimal multilateral wells is a multivariable problem, which is a function of determining the best well locations and trajectories in a hydrocarbon reservoir with the ultimate objectives of maximizing productivity and recovery. The placement of the multilateral wells can be subject to many constraints such as the number of wells required, maximum length limits, and overall economics. This paper introduces a novel technology for placement of multilateral wells in hydrocarbon reservoirs utilizing a transshipment network optimization approach. This method generates scenarios of multiple wells with different designs honoring the most favorable completion points in a reservoir. In addition, the algorithm was developed to find the most favorable locations and trajectories for the multilateral wells in both local and global terms. A partitioning algorithm is uniquely utilized to reduce the computational cost of the process. The proposed method will not only create different multilateral designs; it will justify the trajectories of every borehole section generated. The innovative method is capable of constructing hundreds of multilateral wells with design variations in large-scale reservoirs. As the complexity of the reservoirs (e.g., active forces that influence fluid mobility) and heterogeneity dictate variability in performance at different area of the reservoir, multilateral wells should be constructed to capture the most productive zones. The new method also allows different levels of branching for the laterals (i.e., laterals can emanate from the motherbore, from other laterals or from subsequent branches). These features set the stage for a new generation of multilateral wells to achieve the most effective reservoir contact.

scholarly journals Modeling the Depositional Environment of the Sandstone Reservoir in the Middle Miocene Sidri Member, Badri Field, Gulf of Suez Basin, Egypt: Integration of Gamma-Ray Log Patterns and Petrographic Characteristics of Lithology

2020 ◽  
Author(s):  
Ahmed E. Radwan
Keyword(s):  
Depositional Environment ◽  
Middle Miocene ◽  
Gamma Ray ◽  
Hydrocarbon Reservoirs ◽  
Gulf Of Suez ◽  
Hydrocarbon Reservoir ◽  
Core Samples ◽  
Phase Cycle ◽  
Sandstone Reservoir ◽  
Depositional Setting

Abstract Understanding the depositional setting of siliciclastics reservoir is fundamental process to exploration and development of hydrocarbon reservoirs and to the multi-phase cycle of the oil and gas industry. Typically, core samples from existing or potential reservoirs can be used for interpretation of depositional environment. However, the lack of core samples in certain reservoirs represents a challenge for reservoir development plans and further exploration. To overcome the absence of core samples in the middle Miocene Sidri Member in the Badri field, Gulf of Suez, this study aimed to reconstruct its depositional settings by coupling well logging patterns and petrographic characterization of ditch cuttings. Consequently, 30 thin section samples representing the reservoir section of four wells were described in detail and standard petrographic characteristics were determined. Then, gamma-ray (GR) log patterns of the studied reservoir wells were used for interpretation of depositional environment. Petrographic analysis indicates that the sandstone reservoir is fine- to medium-grained arkose with dominant siliceous cement and composed mainly of quartz, feldspars, and lithic fragments. Pores reflecting primary and secondary porosity as well as inter-granular pores are dominant. The facies analysis indicates that the studied member has a heterogeneous nature and rapid facies change; its main lithofacies are blocky sandstones, intercalated sandstones and shales, and shales. Three electrofacies were interpreted in the studied section: (1) electrofacies-A (blocky sandstones), which is characterized by a cylindrical-shaped (right boxcar) GR trend; (2) electrofacies-B (intercalated sandstones and shales), which is characterized by an irregular log trend with serrated shape; and (3) electrofacies-C (shales), which is characterized by irregular GR trend and has no character. The interpreted results indicate a tidal channel depositional setting for electrofacies-A, mixed tidal flat depositional setting for electrofacies-B, and low relief offshore mudstone depositional setting for electrofacies-C. Finally, the results illustrate how the coupling of GR patterns with the analysis of petrographic characteristics can be used to understand the depositional setting of a hydrocarbon reservoir that lacks core samples. This work could be useful for assessment of reservoir distribution and quality, for reduction of uncertainty during field development, as well as for providing useful insight to similar hydrocarbon reservoirs elsewhere.

scholarly journals Hydrocarbon reservoir delineation using simultaneous and elastic impedance inversions in a Niger Delta field

2021 ◽  
Author(s):  
Oluwakemi Y. Adesanya ◽  
Lukumon Adeoti ◽  
Kayode F. Oyedele ◽  
Itsemode P. Afinotan ◽  
Taiwo Oyeniran ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Wave Velocity ◽  
Seismic Data ◽  
Niger Delta ◽  
Low Frequency ◽  
Hydrocarbon Reservoirs ◽  
Well Test ◽  
Simultaneous Inversion ◽  
Hydrocarbon Reservoir ◽  
Elastic Impedance

AbstractThe global energy demand is increasing while production from mature fields is drastically reducing consequently, oil and gas industries are expanding activities into more challenging areas. The inability of the traditional seismic data to properly delineate hydrocarbon reservoirs from subtle seismic features in ‘Sandfish’ field located offshore, Niger Delta informed the use of simultaneous and elastic impedance inversion. The elastic and derived volumes from seismic inversion would reduce risk, enhance hydrocarbon discovery and optimize development plans in the study area. Four ‘Sandfish’ (Sfn) wells (Sfn-01, Sfn-02, Sfn-04 and Sfn-05), check-shots and 3D seismic data of five angle stacks (6–12°, 12–18°, 18–26°, 26–32° and 32–42°) were used in the study. Low frequency (0–2 Hz) models were generated from interpolation of high-cut-filtered compressional wave velocity log (P-sonic), shear wave velocity log (S-sonic) and density log guided by interpreted four seismic horizons. The low frequency models broaden the spectrum of the elastic volumes and also served as inversion constraints. The five partial angle stacks varying from 6–42° were simultaneously inverted using Jason’s Rock-Trace® inversion software which iterated trial inversions until the model sufficiently matched the seismic data. The near (6–12°) angle and far-far (32–42°) angle stacks were also inverted and compared with the inverted volumes from the simultaneous inversion. This was carried out to determine the effectiveness of near and far-far elastic impedance volume in delineating hydrocarbon reservoirs. The inverted elastic volumes P-impedance (ZP), S-impedance (ZS), density (ρ), near and far-far elastic and derived volumes lambda-rho (λρ), mu-rho (µρ), Poisson’s-ratio (σ) reveal vertical and lateral continuity of the reservoirs identified (K01, N01 and P01) at 2179 m, 2484 m and 3048 m, respectively. The delineated reservoirs showed good match with the sand tops away from the well control validated by a blind well test. The cross-plot of inverted ZP from simultaneous inversion and well ZP gave correlation coefficient of 86% indicative of high quality inverted volume which will reduce exploration risk. The plot of inverted ZP from simultaneous inversion and inverted far-far elastic volume reflected 82% correlation coefficient indicating that this method could be adopted in other fields with limited data and similar geological setting. Hence, the study has shown the efficacy of elastic volumes in delineating hydrocarbon reservoirs which can help locate optimum region for development wells.

IMPROVED SYSTEM OF PROJECT-ORIENTED MANAGEMENT OF THE DEVELOPMENT OF HYDROCARBON RESERVOIRS

2020 ◽  
Vol 2020 (1) ◽  
pp. 4-8
Author(s):  
Dmitry Zavyalov
Keyword(s):  
Management System ◽  
Real Data ◽  
Hydrocarbon Reservoirs ◽  
Development Management ◽  
Hydrocarbon Reservoir ◽  
Reservoir Development

Management of hydrocarbon reservoir development is associated with the continuous implementation of many projects and it is project-oriented management. Many provisions of the existing hydrocarbon reservoir development management system are regulated, however, there is a contradiction between the need to take into account all aspects of management in a complex and the lack of such an opportunity in the existing management system. The work presents an improved system of project-oriented management of hydrocarbon reservoir development, as well as the results of its testing on real data in comparison with the existing management system.

scholarly journals Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
R. Gholami ◽  
A. R. Shahraki ◽  
M. Jamali Paghaleh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Hydrocarbon Reservoirs ◽  
Petroleum Engineering ◽  
General Regression Neural Network ◽  
Support Vector ◽  
Well Test ◽  
Hydrocarbon Reservoir ◽  
Testing Dataset ◽  
Core Permeability

Permeability is a key parameter associated with the characterization of any hydrocarbon reservoir. In fact, it is not possible to have accurate solutions to many petroleum engineering problems without having accurate permeability value. The conventional methods for permeability determination are core analysis and well test techniques. These methods are very expensive and time consuming. Therefore, attempts have usually been carried out to use artificial neural network for identification of the relationship between the well log data and core permeability. In this way, recent works on artificial intelligence techniques have led to introduce a robust machine learning methodology called support vector machine. This paper aims to utilize the SVM for predicting the permeability of three gas wells in the Southern Pars field. Obtained results of SVM showed that the correlation coefficient between core and predicted permeability is 0.97 for testing dataset. Comparing the result of SVM with that of a general regression neural network (GRNN) revealed that the SVM approach is faster and more accurate than the GRNN in prediction of hydrocarbon reservoirs permeability.

COUNTING TOTAL BACTERIA IN LAND ORGANIC WASTE HOUSEHOLD AND LAND INORGANIC WITH TOTAL PLATE COUNT METHOD (TPC)

2017 ◽  
Vol 4 (2) ◽  
pp. 87-91
Author(s):  
Ekamaida Ekamaida
Keyword(s):  
Soil Fertility ◽  
Bacterial Population ◽  
Organic Soil ◽  
Biological Properties ◽  
Plate Count ◽  
Bacterial Populations ◽  
Fertility Data ◽  
Plate Count Method ◽  
The Mean ◽  
Total Bacteria

The soil fertility aspect is characterized by the good biological properties of the soil. One important element of the soil biological properties is the bacterial population present in it. This research was conducted in the laboratory of Microbiology University of Malikussaleh in the May until June 2016. This study aims to determine the number of bacterial populations in soil organic and inorganic so that can be used as an indicator to know the level of soil fertility. Data analysis was done by T-Test that is by comparing the mean of observation parameter to each soil sample. The sampling method used is a composite method, which combines 9 of soil samples taken from 9 sample points on the same plot diagonally both on organic soil and inorganic soil. The results showed the highest bacterial population was found in total organic soil cfu 180500000 and total inorganic soil cfu 62.500.000

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