Thermal Enhanced Oil Recovery Processes Research Articles

Molecular insights into the synergistic mechanisms of hybrid CO2-surfactant thermal systems at heavy oil-water interfaces

A hybrid thermal recovery process is a promising EOR (Enhanced Oil Recovery) method and has been widely applied to post-steamed heavy oil reservoirs. Due to the complexity of High Temperature and High Pressure (HTHP) reservoir environments, Molecules Dynamics (MD) simulations are conducted to investigate the interfacial behavior between multiphase multicomponent fluids and heavy oil over a wide temperature range. We select CO2 and a cost-effective anionic surfactant, SDS (Sodium Dodecyl Sulfate), to build hybrid thermal systems. The interfacial behavior in different systems is characterized by the local density distribution, cluster analysis, hydrogen bond characters, solvent accessible surface area, and interaction energy. Systematic MD simulations reveal a two-step interfacial tension (IFT) reduction process stimulated by thermal migration. Both CO2 and SDS can achieve an IFT decrement with different mechanisms. Moreover, a co-addition of CO2 and SDS can further efficiently reduce the IFT. Average Reduced Density Gradient (aRDG) results reveal that a unique asphaltenes-SDS-CO2 structure improves the interfacial activities and mobility of the heavy oil phase through a bidirectional mass exchange mechanism, which ultimately realizes the most significant IFT decrement. Furthermore, an optimal concentration (10C–20S) in a hybrid thermal system is recommended because of its balanced performance. This work sheds insight into the synergistic effects of multiphase multicomponent thermal fluids on hybrid thermal systems, which will guide future field applications of hybrid thermal EOR processes.

Simultaneous CO2 adsorption and conversion over Ni-Pd supported CeO2 nanoparticles during catalytic n-C7 asphaltene gasification

Shallow heavy oil reservoirs are suitable for the geo-sequestration of CO2 based on its adsorption and conversion during thermal-enhanced oil recovery processes. This study aims to evaluate multifunctional nanomaterials for CO2 adsorption and its subsequent transformation into valuable sub-products during the catalytic decomposition of asphaltenes in a steam gasification atmosphere. Three ceria nanoparticles with cubic (C-CeO2), orthorhombic (O-CeO2), and spherical (S-CeO2) shapes were tested in four stages including i) CO2 adsorption at 30, 50, 100, and 200 °C between 0.084 and 3.0 MPa, ii) dynamic in-situ CO2 adsorption in the presence of steam between 170 and 230 °C at 3.0 MPa, iii) dynamic in-situ CO2 adsorption in the presence of steam with adsorbed asphaltenes between 170 and 230 °C at 3.0 MPa and iv) CO2 conversion at the same conditions. The best nanoparticle morphology was doped with 1 wt% of Ni and Pd (C-CeNiPd) and was tested in the same experiments. Among the most important results, CO2 adsorption increased in the order S-CeO2 < O-CeO2 < C-CeO2 < C-CeNiPd, regardless of the temperature. When steam was injected, CO2 adsorption is reduced in all the systems. At 200 °C adsorption decreased by 3.0 %, 2.7 %, 2.5 %, and 2.0 % in processes assisted by S-CeO2, O-CeO2, C-CeO2, and C-CeNiPd, respectively. Nanoparticles with adsorbed asphaltenes presented a high tendency for CO2 adsorption as well. The nanoparticle with the best morphology (C-CeO2) adsorbed about 24.3 % (3.92 mmol) of CO2 at 200 °C, and the yield increased after doping with Ni and Pd, obtaining CO2 adsorption of 34 %. Finally, for CO2 conversion, a mixture of gases composed of CO, CH4, H2, and light hydrocarbons (LHC) is obtained. The hydrogen production content follows a trend that agrees well with each material's adsorptive capacity and catalytic activity. The maximum %vol of H2 produced at 200 °C during asphaltene gasification was 31 %, 29 %, 24 %, and 23 % for C-CeNiPd, C-CeO2, O-CeO2, and S-CeO2, respectively.

In-Situ Steam Generation Using Mist Water-Air Injection as Enhanced Oil Recovery and Energy Efficiency Process: Kinetic Modeling and Numerical Simulation Approach

Summary In the current energy transition era, oil exploitation and especially the development of heavy oil reservoirs are facing big challenges to overcome the possible limitations in terms of economy (oil price), energy efficiency, and carbon footprint. Particularly, thermal enhanced oil recovery processes need to be re-evaluated in an attempt to harness the injected and produced energy. In that sense, Ecopetrol is evaluating new strategies to optimize the current steam injection process using different hybrid technologies from laboratory to field scale. One of the most attractive initiatives is evaluating the in-situ steam generation using mist water-air injection. This process involves simultaneous air and water injection into the formation through a set of nozzles. It looks to use part of the in-situ oil as a fuel, using the reservoir not only as a tank of energy but also as a steam generator. The main contribution of the technique concerning conventional steam generation is the use of the heat from the combustion of the residual oil to generate an in-situ steam front to transfer the uncontacted oil. This is reflected in reduced carbon dioxide (CO2) emissions, reduced fuel and water requirements, and increased oil production and net energy recovery. This article describes the methodology, results, history matching, and kinetic modeling of experimental evaluations and the upscaling of the experimental observations to a representative sector model from a Colombian heavy oil field. Results are described in terms of incremental oil recovery, energy efficiency, and carbon intensity compared with the baseline (a traditional steamflooding scenario). The technology of in-situ steam generation using mist waterair injection led to benefits in terms of better energy use and reducing the external fuel dependency for steam generation at the surface. Additionally, it was possible to identify improvements in incremental oil recovery (around 90%), energy efficiency (about 10 times less energy required to produce 1 m3 of oil), and reduction in carbon intensity (up to 91%) considering as baseline a conventional steamflooding scenario. These results will be key input parameters for designing and commissioning future applications in the Colombian fields.

Power from Geothermal Resources as a Co-product of the Oil and Gas Industry: A Review.

The increase in the global demand for energy and fossil fuel dependency is hindering efforts to reduce greenhouse gas (GHG) emissions. Geothermal resources supplement this increase in energy demand with reduced emissions because of their availability, base-load production profile, and climatic independence. Despite these advantages, the development of geothermal energy is limited because of different reasons such as subsurface exploration risk and high upfront capital cost for drilling and facility construction. However, similarities in infrastructure and operations between the oil and gas industry and the geothermal industry can optimize expense and development when exploiting geothermal resources. Thus, in this review, we present recent advances and applications of geothermal power systems in the oil and gas industry starting from the fundamentals and basic principles of geothermal energy and the organic Rankine cycle (ORC). These applications include the use of geothermal resources via abandoned wells, active wells, and paired thermal enhanced oil recovery processes with injection for fluid heating and energy production. Abandoned wells are alternatives that reduce costs in geothermal energy-use projects. The use of geothermal resources via active wells allows the valorization of a resource, such as the production of water, which is considered a byproduct of production activities in an oilfield. The use of thermally enhanced oil recovery processes enhances the energy conditions of fluids produced in the field, improving geothermal systems with fluids at higher temperatures. Finally, an overview is presented of the challenges and opportunities of geothermal energy in the oil industry where the requirement to improve the usage of technologies, such as the ORCs, with the working fluids used in the cycles, is highlighted. Furthermore, the importance of environmental studies and use of novel tools, such as nanotechnology, to improve the efficiency of geothermal energy usage is highlighted.

Molecular composition of low-temperature oxidation products in a simulated crude oil In-situ combustion

In-situ combustion (ISC) is a thermal enhanced oil recovery process in which energy is generated by the combustion of a portion of the oil within the reservoir. Low temperature oxidation (LTO) is one of the dominant mechanisms controlling fuel availability in ISC and affects the physical properties of the crude oil, thus the characterization of the crude oil and its LTO products are essential for a better understanding of the ISC process. In this study, a thermal oxidation experiment of a heavy crude oil was carried out to investigate the molecular changes associated to coke formation during ISC. More than half of the component was depleted by combustion or thermal pyrolyzed out, leaving a coke-like solid in the reactor with a yield of a quarter by weight of the crude oil. More than 90% of the coke-like solid is toluene soluble. Condensate oil expelled from the reactor was collected with a yield of about 15 wt% of the crude oil. Small-molecule oxidation products, such as ketones, fatty acids, phenolic compounds were identified in the condensate by GC–MS. High-resolution mass spectrometry results showed that the molecular composition of oxygen- and sulfur-containing compounds changed largely, with the generation of highly oxygenated compounds and sulfoxides. A substantial portion of large-molecule hydrocarbons were survived and enriched in the residue. The main components in the toluene insoluble substance, namely oxidation coke, are methanol soluble and composed of highly oxygenated compounds. The results indicate that oxidation and thermal cracking are both the important reaction in the simulated ISC, the molecular composition is instructive for the understanding of pyrolysis and oxidation behavior of coke formation from the crude oil.

Effect of temperature on two-phase gas/oil relative permeability in unconsolidated sands

Thermal Enhanced Oil Recovery (TEOR) for heavy oil reservoirs involves the simultaneous flow of oil and steam, mimicking the gaseous phase with an elevation in the temperature. While implementing the TEOR process, reservoir performance prediction tools require the water/oil and gas/oil relative permeability data. Many studies reported the temperature effect on two-phase water/oil relative permeability despite knowing that steam or vapor is conventionally injected, mimicking the gaseous phase during heavy oil recovery. Thus, this requires the knowledge of temperature effect on two-phase gas/liquid relative permeability as well. However, limited studies are reported in the literature regarding the temperature dependency of relative permeability in gas/liquid systems; offer no census about its effects. The scarcity of such studies in the literature is due to the lack of systematic experimental studies and complications observed while conducting the higher temperature tests to mimic the thermal EOR. Hence, the objective of this study was to examine the temperature effect on two-phase gas/liquid relative permeability at varying temperatures from 64 to 132 °C using a systematic and reliable experimental process.In this study, Poly Alpha Olefin (PAO-100) was used as the oleic phase, deionized water as the immobile phase, and nitrogen gas as the displacing phase in a clean unconsolidated sandpack under the confining pressure around 1000 psi. Furthermore, Johnson-Neumann-Bossler (JBN) method was opted to interpret the two-phase gas/liquid relative permeability curves from the displacement data, i.e., cumulative oil production and pressure drop measured across the sandpack. The experimental observation suggests that irreducible water saturation and endpoint oil relative permeability are temperature independent. The residual oil saturation decreased with the increase in temperature and led to a higher endpoint relative permeability to gas. On the other hand, the oil relative permeability at equal saturation uplifted, suggesting the enhanced mobility of oil through pores with increasing temperature. On the other side, the gas relative permeability at equal saturation was temperature-insensitive other than at residual oil saturation. Also, the two-phase flow region increased with the rise in temperature as both the relative permeability curves shifted upwards, and the broader curve was observed. Hence, this study strongly suggests that the reservoir engineers or commercial reservoir simulators should account for the temperature effects on two-phase gas/oil relative permeability to efficiently predict heavy oil reservoir performance and management during the thermal enhanced oil recovery process (TEOR).

Improving oil recovery rates in THAI in situ combustion process using pure oxygen

In-situ-combustion-type processes such as the Toe-to-Heel Air Injection (THAI) process have been shown to have advantages over other thermal enhanced oil recovery processes. The THAI process uses in situ combustion to mobilise and upgrade bitumen or heavy oil within the reservoir. This study compares the effect of injecting pure oxygen instead of air on the performance of the THAI process. It is found that over the 833 days of the process time, an additional 3.85% of oil originally in place (OOIP) was recovered with injecting pure oxygen compared to with injecting air. It is found that as the combustion front advances, higher axial length fraction of the HP well is used for oil production. Observing the oil production rate distribution along the HP well, it is concluded that highest oil flow rate enters the HP well at the toe in both models. It has been shown that it is likely that gas production might have negatively affected the oil production when air is the injected fluid (i.e. in the air model).

Multi-level delumping strategy for thermal enhanced oil recovery simulations at low pressure

We present a multi-level delumping method suitable for thermal enhanced oil recovery processes. At low pressures, the temperature variable is the most critical factor impacting the displacement process through viscosity reduction and evaporation/condensation effects. Hydrocarbon components are vaporized under high temperatures, move downstream in the gas phase and condense back to the liquid phase. That process is governed by the K-values of the components, evaporating out of the liquid phase sequentially with increasing temperatures. To reduce the computational cost, it is standard practice to reduce the number of (pseudo-) 6 components used in thermal reservoir simulation. Depending on the number and type of hydrocarbon pseudo-components retained in the simulations, we may not be able to capture the correct displacement due to large errors in the lumped phase behavior (flash) computations. We address that problem through a multi-level method: we use data obtained from a short simulation using the most detailed fluid description available, and leverage that information to guide a delumping process. We use temperature as a proxy variable for composition, and select reference temperatures. We extract the corresponding reference compositions from the detailed run and use them to extend the lumped pseudo-components to an approximate detailed composition. We compute the phase mole fractions as well as the gas compressibility factor. We test our method using six heavy oil samples, and under two different recovery processes: hot nitrogen injection and in-situ combustion (air injection and exothermic oxidation reactions). The average error on the liquid mole fraction is reduced by 4–12 times (depending on the oil samples) compared to the flash using pseudo-components, and the maximum error by 6–48 times. We illustrate that the method is amenable to manually adding more information about the physics of some oil samples. We also discuss how to efficiently pick the reference temperatures. For uniformly sampled temperatures (between a minimum and maximum temperature), we conduct a sensitivity study which led us to use six temperatures. We ran both local (Pattern Search, PS) and global (Particle Swarm Optimization, PSO) gradient-free optimization methods. PS is able to find the closest local minimum to the uniform set, giving a limited improvement of 6.5%. The known increased cost for PSO is worth the investment in at least one of the cases we considered, leading to a 67% improvement.

Nanotechnology Applied to Thermal Enhanced Oil Recovery Processes: A Review

The increasing demand for fossil fuels and the depleting of light crude oil in the next years generates the need to exploit heavy and unconventional crude oils. To face this challenge, the oil and gas industry has chosen the implementation of new technologies capable of improving the efficiency in the enhanced recovery oil (EOR) processes. In this context, the incorporation of nanotechnology through the development of nanoparticles and nanofluids to increase the productivity of heavy and extra-heavy crude oils has taken significant importance, mainly through thermal enhanced oil recovery (TEOR) processes. The main objective of this paper is to provide an overview of nanotechnology applied to oil recovery technologies with a focus on thermal methods, elaborating on the upgrading of the heavy and extra-heavy crude oils using nanomaterials from laboratory studies to field trial proposals. In detail, the introduction section contains general information about EOR processes, their weaknesses, and strengths, as well as an overview that promotes the application of nanotechnology. Besides, this review addresses the physicochemical properties of heavy and extra-heavy crude oils in Section 2. The interaction of nanoparticles with heavy fractions such as asphaltenes and resins, as well as the variables that can influence the adsorptive phenomenon are presented in detail in Section 3. This section also includes the effects of nanoparticles on the other relevant mechanisms in TEOR methods, such as viscosity changes, wettability alteration, and interfacial tension reduction. The catalytic effect influenced by the nanoparticles in the different thermal recovery processes is described in Sections 4, 5, 6, and 7. Finally, Sections 8 and 9 involve the description of an implementation plan of nanotechnology for the steam injection process, environmental impacts, and recent trends. Additionally, the review proposes critical stages in order to obtain a successful application of nanoparticles in thermal oil recovery processes.

Simulation study on the effect of reservoir bottom water on the performance of the THAI in-situ combustion technology for heavy oil/tar sand upgrading and recovery

Some of the bitumen/tar sand/heavy oil reservoirs are underlain by bottom water (BW) layer, which often severely affects the performance of thermal EOR (enhanced oil recovery) processes. The effect of bottom water on the performance of the toe-to-heel air injection (THAI) in-situ combustion (ISC) process is investigated through reservoir simulation using CMG STARS simulator. The current study has shown that there is a limit to BW thickness above which the performance of the THAI process is affected even though the combustion front propagated stably. It is found that the thickness of the ‘‘basal gas layer’’ (BGL) depends on how further down into the BW zone the horizontal producer (HP) well is located. From this study, it is found that the critical BW thickness, when the THAI process is implemented in any heavy oil BW reservoir with the wells arranged in an SLD (staggered line drive) pattern, should lie in the range of 50% OL (oil layer) < BW (bottom water) < 100% OL (oil layer). A comparative study between the active and non-active aquifers models shows that the same cumulative volume of water is produced and that over the 715 days of the process, only negligible amount of oil is produced from BWN (i.e. static aquifer model). It is found that in neither of the models does oxygen bypass the combustion front and as in the previous studies, both fronts are restricted to the upper part of the reservoir, within the oil zone. Therefore, it follows that even in the presence of active aquifer (i.e. BWA model), the THAI process still operates stably in terms of combustion front propagation and sustenance. For the combustion initiated at the oil–water (O–W) interface, it is found that controlled gravity override resulted in a high rate of advancement of combustion front at the top of the reservoir. The combustion is observed to not propagate along the BGL, rather, it propagates as though it is initiated at the top of the reservoir. It is shown that the BGL is only formed during the early period of air injection as the combustion gases could not reach the HP well without displacing the water to create initial gas flow pathway into the HP well. It is also observed that initiating the combustion at the oil–water interface results in a massively improved oil recovery rates, most especially when implemented in the DLD (direct line drive) pattern.

Toward mechanistic understanding of Fast SAGD process in naturally fractured heavy oil reservoirs: Application of response surface methodology and genetic algorithm

Thermal recovery is one of the most common enhanced oil recovery (EOR) techniques in heavy oil reservoirs. Fast-SAGD method is a novel SAGD approach in which offset wells are drilled for periodic injection and production. This reduces the costs of SAGD pair injection-production wells and results in more production in a shorter time. Since production from heavy oil reservoirs is becoming more and more important, this study focused on thermal recovery of Mond field as one of the heavy oil fields in Iran. In this study, effective parameters of Fast-SAGD process including injection pressure and rate, injection, production, and offset well heights, offset well pressure and its injection and production periods were studied. The main purpose of this research was solving the problem of Fast-SAGD process optimization in terms of time and accuracy, as similar studies were not systematically studied on optimization of this process. In previous studies, effective parameters on the objective function were considered independently for obtaining the optimum conditions. Genetic algorithm was applied for optimization. The objective function was selected as a combination of recovery factor (RF) and cumulative steam to oil ratio (CSOR). To speed-up the optimization, effective variables were converted to discrete values. Optimization results using discrete and continuous variables were compared to each other. Moreover, results were compared to previously optimization technique applied in this field by Hemmati et al. (2013). Another point in this study was sensitivity analysis application. This showed that reservoir parameters are dependent on each other and traditional methods are inefficient for optimization of complex heavy oil reservoirs and thermal EOR processes. These points led to better results in terms of time and accuracy in this model. Results indicated 3% increase in RF and 4.7% decrease in CSOR for one injection cycle approach in comparison to two-cycle case. Response surface methodology (RSM) was introduced to setup a mathematical basis for effective variables and their interactions. Optimization results using analysis of variance (ANOVA) denoted that the injected steam and RF were 10% and 6% less in comparison to the results of genetic algorithm, respectively.

Modeling temperature-based oil-water relative permeability by integrating advanced intelligent models with grey wolf optimization: Application to thermal enhanced oil recovery processes

Relative permeability is an indispensable parameter that is involved in many sensitive tasks related to reservoir studies. Due to this fact, there is increasing interest about methods that can accurately capture the temperature effect on relative permeability. As the existing empirical methods suffer from inaccuracies and the experimental methods are expensive and time consuming, developing accurate, rapid, and inexpensive model is inevitable to determine temperature-dependent oil/water relative permeability. In this paper, we propose various intelligent approaches to predict the effect of temperature on relative permeability in the oil/water systems (Kro and Krw). These approaches are based on two machine-learning methods: least square support vector machine (LSSVM) and radial basis neural network (RBFNN), coupled with four metaheuristic algorithms including particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE) and grey wolf optimization (GWO). A large database including 1223 experimental points collected from published literature and some Algerian fields is employed to establish and test the models with the following inputs: absolute permeability, water saturation, reservoir temperature, water viscosity, and oil viscosity. The obtained results revealed that the proposed models can predict the temperature-based oil/water relative permeability with very satisfactory accuracy. Furthermore, graphical and statistical error analyses illustrated that RBFNN-GWO model outperforms the existing correlations and the other established hybridization-based models in predicting both Kro and Krw, where RBFNN-GWO showed overall determination coefficients (R2) of 0.9997 and 0.9996, average absolute relative deviations (AARDs%) of 3.4056 and 5.8663, and root mean squared errors (RMSEs) of 0.0073 and 0.0048 for Kro and Krw, respectively. Finally, outliers detection using the Leverage approach confirmed that the proposed RBFNN-GWO model is statistically valid, where only 1.69% and 1.39% of the Kroand Krw data points, respectively, may be regarded as doubtful data.

Generation of hydrogen sulfide during the thermal enhanced oil recovery process under superheated steam conditions.

During the thermal enhanced oil recovery (EOR) process, the hazardous hydrogen sulfide (H2S) gas among the produced gases causes significant difficulty in the exploration and development of petroleum. In this study, the effects of superheat degree on the H2S generation by heavy oil aquathermolysis were explored through simulated experiments. The crude and residual oils before and after the reaction were separated into saturate, aromatic, resin and asphaltene fractions (SARA). The oil samples were analyzed from various perspectives by various characterization methods including Fourier transform infrared (FTIR) spectroscopy, elemental analysis, and X-ray photoelectron spectroscopy (XPS). The results showed that H2S generation was favored by larger superheat degree at the same temperature, and it increased from 0.178 to 0.345 mL g−1 oil with an increase in the superheat degree from 62.19 to 89.42 °C. The contents of the sulfur-containing substances, which were supposed to be the main sources of H2S generation, in the saturate and aromatic fractions decreased significantly with an increase in the superheat degree; the increase in the superheat degree led to a slight reduction in the contents of the methylene, methyl and carboxyl/carbonyl groups. Moreover, the analysis of the main sulfur existing forms before and after the reaction suggests that sulfur in the forms of sulfides, sulfones and sulfates is more likely to generate H2S under superheated steam conditions. This study provides an understanding of the mechanism of H2S generation during the process of injecting superheated steam for heavy oil recovery.

Modeling temperature dependency of oil - water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming

ABSTRACTIn the implementation of thermal enhanced oil recovery (TEOR) techniques, the temperature impact on relative permeability in oil–water systems (Krw and Kro) is of special concern. Hence, developing a fast and reliable tool to model the temperature effect on Krw and Kro is still a major challenge for precise studying of TEOR processes. To reach the goal of this work, two promising soft-computing algorithms, namely Group Method of Data Handling (GMDH) and Gene Expression Programming (GEP) were employed to develop reliable and simple to use paradigms to predict the temperature dependency of Krw and Kro. To do so, a large database encompassing wide-ranging temperatures and fluids/rock parameters, was considered to establish these correlations. Statistical results and graphical analyses disclosed the high degree of accuracy for the proposed correlations in emulating the experimental results. In addition, GEP correlations were found to be the most consistent with root mean square error (RMSE) values of 0.0284 and 0.0636 for Krw and Kro, respectively. Lastly, the performance comparison against the preexisting correlations indicated the large superiority of the newly introduced correlations. The findings of this study can help for better understanding the temperature dependency of Krw and Kro in TEOR.

Phased Pilot Approach Reduces Uncertainty in Carbonate Steamflood Development

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 191695, “Results From a Phased Piloting Approach in the First Eocene Reservoir of Wafra: Reducing Uncertainties in Carbonate Steamflood Development,” by David Barge, SPE, Joel Brown, Raushan Kumar, Chris Lolley, SPE, Ruurd Bartlema, and Saleh Al-Ghamdi, Chevron, prepared for the 2018 SPE Annual Technical Conference and Exhibition, Dallas, 24–26 September. The paper has not been peer reviewed. The First Eocene is a multibillion-barrel heavy-oil carbonate reservoir in the Wafra field, located in the Partitioned Zone (PZ) between Saudi Arabia and Kuwait. After more than 60 years of primary production, expected recovery is low and provides a good target for enhanced-oil-recovery (EOR) processes. A phased piloting approach has been used to reduce the uncertainties (subsurface and surface) related to application of thermal EOR processes in this field. Introduction Wafra is one of four major fields located in the PZ (Fig. 1). Because of the low primary oil recovery and large original oil in place of the Wafra Eocene reservoirs, a significant EOR opportunity exists. A detailed geological description of the reservoir is provided in the complete paper. Because of key surface and sub-surface uncertainties, along with the high cost of implementing a full-field steamflood-development (FFSFD) project, a phased, sequential approach, from piloting to FFSFD, was implemented. This approach was required so that critical uncertainties could be evaluated efficiently and sequentially in a time-frame consistent with overall reservoir- development objectives while managing capital exposure. Pilot Planning and Design Steamflood Pilots. A specific set of objectives, primary and secondary, was developed for each of the continuous-steam-injection (CSI) pilots. Primary objectives were identified as being critical for progression to the next phase (piloting/FFSFD); secondary objectives were regarded as important in understanding identified uncertainties, but not necessarily project roadblocks. Success measures for the pilots were derived from the key metrics that were prerequisites for the next phase. These success measures were formulated to ensure that learnings gained will be used to support the design and operational planning of the next phase. A five-spot pattern on a 1.25-acre steamflood-pilot, small-scale test (SST) was a continuation of the phased approach in managing and mitigating technical uncertainties related to implementing steamflooding. The primary objectives of the SST were to test whether Eocene-produced water could be treated to produce suitable feedwater for steam generation and to test steam injectivity in the First Eocene reservoir. The SST was conducted in First Eocene C Zone/Steam Zone 3 and successfully proved sustained injectivity using steam generated from the produced water. The SST was a pre-lude to a multipattern steamflood pilot known as the large-scale pilot (LSP).

In-situ upgrading of reservoir oils by in-situ preparation of NiO nanoparticles in thermal enhanced oil recovery processes

In-situ combustion (ISC) in the presence of catalytic metal oxide nanoparticles is a promising enhanced oil recovery process for heavy oil reservoirs. A proper method for uniform distribution of the nanoparticles in the reservoir fluids is in-situ synthesis of the nanomaterials. Precursor metal ionic aqueous solutions and heavy oil form water/oil emulsions, downstream of the combustion front, by the aid of asphaltenes and resins. Upon heating, the nanoparticles are formed, on which asphaltenes are adsorbed and pyrolyzed to provide the fuel for a stable ISC front. NiO nanoparticles were synthesized via the in-situ synthesis process. Ni(NO3)2·6H2O aqueous solution was dispersed in heavy oil model solutions of asphaltenes in toluene-vacuum gasoil. The droplet size, stability, and interface elasticity of the emulsions were monitored by changing the precursor salt concentration, salt solution/oil ratio, and temperature and pressure. Bottle test, micropipette test, microscope imaging, and equilibrium and dynamic IFT measurements were used to characterize the emulsions. The stable emulsions were exposed to high-temperature and high-pressure conditions to deliver nanoparticles. The in-situ prepared coked nanoparticles were characterized by XRD, FTIR, HRTEM, and TGA techniques and compared with ex-situ prepared NiO nanoparticles. Prior and subsequent to the in-situ synthesis, the kinematic viscosity of the model oil, as an upgrading index, was measured. It is found that the emulsions droplet size increases with the precursor salt concentration, at constant salt solution/oil ratios, leading to instability of the emulsions. An optimum value is found for the salt solution/oil ratio, at constant precursor molarities, resulting in stable emulsions. Compared to ex-situ prepared nanoparticles, the in-situ synthesized NiO nanoparticles exhibit around 48% higher coke formation tendency and shift the coke combustion temperature by about 130°C downward. Furthermore, the viscosity of the model oil decreases by 2–8% during the in-situ synthesis procedure, showing an in-situ upgrading potential for the process.

Effective Thermal Conductivity Modeling of Sandstones: SVM Framework Analysis

Among the most significant physical characteristics of porous media, the effective thermal conductivity (ETC) is used for estimating the thermal enhanced oil recovery process efficiency, hydrocarbon reservoir thermal design, and numerical simulation. This paper reports the implementation of an innovative least square support vector machine (LS-SVM) algorithm for the development of enhanced model capable of predicting the ETCs of dry sandstones. By means of several statistical parameters, the validity of the presented model was evaluated. The prediction of the developed model for determining the ETCs of dry sandstones was in excellent agreement with the reported data with a coefficient of determination value (\({R}^{2})\) of 0.983 and an average absolute relative deviation of 0.35 %. Results from present research show that the proposed LS-SVM model is robust, reliable, and efficient in calculating the ETCs of sandstones.

Copper oxide nano-fluid stabilized by ionic liquid for enhancing thermal conductivity of reservoir formation: Applicable for thermal Enhanced Oil Recovery processes

Since the oil reservoirs are limited and energy demand is increasing, seeking for high efficient EOR processes or enhancing the efficiency of current proposed EOR methods for producing trapped oil from reservoirs are highly investigated. As a way out, it is possible to couple the EOR and nanotechnology to utilize the efficiency of both methods together. Regarding this possibility, in the current study, in the first stage of investigation stable and uniform water-based solution of nano size particles of copper oxide with different concentrations (0.01-0.05 M) were prepared and then injected into the core samples. In the first stage, the effects of different surfactants respect to their concentrations was investigated. Then, different scenarios of using nano-fluid as a thermal conductivity modifier were examined. The obtained results clearly demonstrate that changing concentration of nano particles of copper oxide from 0.01 M to 0.05 M is able to enhance the thermal conductivity of rocks from 27 % to 48 % compared with the thermal conductivity of dry core.

Copper Oxide Nano-Fluid Stabilized by Ionic Liquid for Enhancing Thermal Conductivity of Reservoir Formation; Applicable for Thermal Enhanced Oil Recovery Processes

Copper Oxide Nano-Fluid Stabilized by Ionic Liquid for Enhancing Thermal Conductivity of Reservoir Formation; Applicable for Thermal Enhanced Oil Recovery Processes

Modeling and analysis of effective thermal conductivity of sandstone at high pressure and temperature using optimal artificial neural networks

Abstract Thermal conductivity (TC) is among the most important characteristics of porous media for hydrocarbon reservoir thermal simulation and evaluating the efficiency of the thermal enhanced oil recovery process. In this study a two-layer artificial neural network (ANN) approach is proposed for estimating the effective TCs of dry and oil saturated sandstone at a wide range of environmental conditions. Temperature, pressure, porosity, bulk density of rock, fluid density and oil saturation are employed as independent variables for prediction of effective TCs of sandstone. Various types of ANN such as multi-layer perceptron (MLP), radial basis function, generalized regression and cascade-forward neural network have been examined and their predictive capabilities are compared. Statistical errors analysis confirms that a two-layer MLP network with seven and 15 hidden neurons are optimal topologies for modeling of TC of oil saturated and dry sandstone, respectively. The predictive capabilities of the optimal MLP models are validated by conventional recommended correlation and a large number of experimental data which were collected from various literatures. The predicted effective TC values have a good agreement with the experimental TC data, i.e., an absolute average relative deviation percent of 2.73% and 3.81% for the overall experimental dataset of oil saturated and dry sandstone, respectively. The results justify the superiority of the optimal MLP networks over the other considered models in simulation of the experimental effective TCs of dry and oil saturated sandstones.