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.

scholarly journals Monitoring cold water injections for reservoir characterization using a permanent fiber optic installation in a geothermal production well in the Southern German Molasse Basin

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Felix Schölderle ◽  
Martin Lipus ◽  
Daniela Pfrang ◽  
Thomas Reinsch ◽  
Sven Haberer ◽  
...  
Keyword(s):  
Reservoir Characterization ◽  
Fiber Optic ◽  
Cold Water ◽  
Dynamical Behavior ◽  
Water Injection ◽  
Vertical Displacement ◽  
Thermal Contraction ◽  
Distributed Temperature Sensing ◽  
Production Well ◽  
Geothermal Fields

AbstractFiber optic sensing has gained importance for wellbore monitoring and reservoir characterization in geothermal fields as it allows continuous, spatially highly resolved measurements. Distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) technologies, among others, enable monitoring of flow regimes and heat transport inside the wellbore to describe the dynamical behavior of the reservoir. The technically challenging installation of a permanent fiber optic monitoring system in a geothermal production well over the entire wellbore length was conducted for the first time at the geothermal site Schäftlarnstraße in Munich, Germany. One cable with two DAS fibers, two DTS fibers, and one fiber for a downhole fiber optic pressure/temperature gauge were clamped to ¾-in. sucker rods and installed to 3.7 km measured depth to collect data from the wellbore after drilling, during testing, and during operations. We present DTS profiles during 3 months of well shut-in and show the results of two cold water injection tests conducted to localize inflow zones in the reservoir and to test the performance of the fiber optic setup. A vertical displacement in temperature peaks of approximately 1.5 m was observed during the injection tests, presumably resulting from thermal contraction of the sucker rod–cable setup. This was verified by analyzing the strain information from the DAS records over 1 h of warm-back after cold water injection with the calculated theoretical thermal contraction of DTS of the same period. We further verified the flowmeter measurements with a gradient velocity analysis of DTS profiles during injection. Intake to the major inflow zone was estimated to 93.5% for the first injection test, respective 94.0% for the second, intake of flowmeter was calculated to 92.0% for the same zone. Those values are confirmed by analyzing DTS profiles during the warm-back period after the well was shut.

scholarly journals Study on Thermo-Hydro-Mechanical Coupling and the Stability of a Geothermal Wellbore Structure

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 649
Author(s):  
Xiaolin Huan ◽  
Gao Xu ◽  
Yi Zhang ◽  
Feng Sun ◽  
Shifeng Xue
Keyword(s):  
Effective Stress ◽  
Cold Water ◽  
Drilling Fluid ◽  
Water Injection ◽  
Coupled Model ◽  
In Situ Stress ◽  
Excess Pore Pressure ◽  
Mechanical Coupling ◽  
Open Hole ◽  
Cement Sheath

For processes such as water injection in deep geothermal production, heat transfer and fluid flow are coupled and affect one another, which leads to numerous challenges in wellbore structure safety. Due to complicated wellbore structures, consisting of casing, cement sheaths, and formations under high temperature, pressure, and in situ stress, the effects of thermo-hydro-mechanical (THM) coupling are crucial for the instability control of geothermal wellbores. A THM-coupled model was developed to describe the thermal, fluid, and mechanical behavior of the casing, cement sheath, and geological environment around the geothermal wellbore. The results show that a significant disturbance of effective stress occurred mainly due to the excess pore pressure and temperature changes during cold water injection. The effective stress gradually propagated to the far-field and disrupted the integrity of the wellbore structure. A serious thermal stress concentration occurred at the junction of the cased-hole and open-hole section. When the temperature difference between the injected water and the formation was up to 160 °C, the maximum hoop tensile stress in the granite formation reached up to 43.7 MPa, as high as twice the tensile strength, which may increase the risk of collapse or rupture of the wellbore structure. The tensile radial stress, with a maximum of 31.9 MPa concentrated at the interface between the casing and cement sheath, can cause the debonding of the cementing sheath. This study provides a reference for both the prediction of THM responses and the design of drilling fluid density in geothermal development.

Development of efficiency increase methods for water injection

2020 ◽  
pp. 57-60
Author(s):  
K.I. Mustafaev ◽  
◽  
◽  
Keyword(s):  
Water Injection ◽  
Cost Effective ◽  
Current Interest ◽  
Residual Oil ◽  
Production Well ◽  
Field Development ◽  
Oil Reserves ◽  
Production Wells ◽  
Efficiency Increase

The production of residual oil reserves in the fields being in a long-term exploitation is of current interest. The extraction of residual oil in such fields was cost-effective and simple technological process and is always hot topic for researchers. Oil wells become flooded in the course of time. The appearance of water shows in production wells in the field development and operation is basically negative occurrence and requires severe control. Namely for this reason, the studies were oriented, foremost, to the prevention of water shows in production well and the elimination of its complications as well. The paper discusses the ways of reflux efficiency increase during long-term exploitation and at the final stages of development to prevent the irrigation and water use in production wells.

scholarly journals Increasing Efficiency of Field Water Re-Injection during Water-Flooding in Mature Hydrocarbon Reservoirs: A Case Study from the Sava Depression, Northern Croatia

2020 ◽  
Vol 12 (3) ◽  
pp. 786 ◽  
Author(s):  
Tomislav Malvić ◽  
Josip Ivšinović ◽  
Josipa Velić ◽  
Jasenka Sremac ◽  
Uroš Barudžija
Keyword(s):  
Water Injection ◽  
Inverse Distance Weighting ◽  
Hydrocarbon Reservoirs ◽  
Water Flooding ◽  
Injection Wells ◽  
Weighting Method ◽  
Hydrocarbon Production ◽  
Recovery Method ◽  
Distance Weighting ◽  
Inverse Distance

The authors analyse the process of water re-injection in the hydrocarbon reservoirs/fields in the Upper Miocene sandstone reservoirs, located in the western part of the Sava Depression (Croatia). Namely, this is the “A” field with “L” reservoir that currently produces hydrocarbons using a secondary recovery method, i.e., water injection (in fact, re-injection of the field waters). Three regional reservoir variables were analysed: Porosity, permeability and injected water volumes. The quantity of data was small for porosity reservoir “L” and included 25 points; for permeability and injected volumes of water, 10 points each were measured. This study defined selection of mapping algorithms among methods designed for small datasets (fewer than 20 points). Namely, those are inverse distance weighting and nearest and natural neighbourhood. Results were tested using cross-validation and isoline shape recognition, and the inverse distance weighting method is described as the most appropriate approach for mapping permeability and injected volumes in reservoir “L”. Obtained maps made possible the application of the modified geological probability calculation as a tool for prediction of success for future injection (with probability of 0.56). Consequently, it was possible to plan future injection more efficiently, with smaller injected volumes and higher hydrocarbon recovery. Prevention of useless injection, decreasing number of injection wells, saving energy and funds invested in such processes lead to lower environmental impact during the hydrocarbon production.

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.

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