Heterogeneous Oil Research Articles

Model of Well Interference During Waterflooding of a Layered Heterogeneous Oil Reservoir within the Framework of the CRM Modeling Concept

Model of Well Interference During Waterflooding of a Layered Heterogeneous Oil Reservoir within the Framework of the CRM Modeling Concept

Study on migration behavior and mechanism of strong carcinogenic PAHs in oil to fume during frying using stable isotope method

The migration and distribution of four strong carcinogenic polycyclic aromatic hydrocarbons (PAH4) in oil to fume during frying were characterized by the stable isotope-labeled PAH4-d12. The results indicated that PAH4-d12 mainly migrated to heterogeneous oil fume, and their mobilities increased significantly with the increase in heating time and frying temperature, which were 0.48−1.08%, 0.96−1.21%, and 2.98−3.60% at 160, 180, and 200 °C, respectively. The established linear models proved that PAH4-d12 migrated through the carrying of heterogeneous fume, and the carrying ability of fume produced at 200 °C was significantly stronger than at 160 and 180 °C. For morphology, ultrafine fume (<1 μm) at 200 °C had the most emission and the strongest carrying ability for PAH4-d12. For components, the heterogeneous fume produced at 200 °C contained more unsaturated fatty acids and conjugated heterocyclic compounds, which could bind with PAH4-d12 in electrostatic force and π-π conjugate force for migration. This study explained the potential mechanism of oil fume components binding to PAH4 from molecular interaction.

An Artificial Neural Network Model for Infill Well Placement and Control Optimization During Foam Injection in Heterogeneous Oil Reservoirs

An Artificial Neural Network Model for Infill Well Placement and Control Optimization During Foam Injection in Heterogeneous Oil Reservoirs

Unstable Coalescence Mechanism and Influencing Factors of Heterogeneous Oil Droplets.

The use of a surfactant solution during oil and gas field development might improve the recovery rate of oil reservoirs. However, the serious emulsification of the produced liquid will bring challenges to the subsequent treatment process and storage and transportation. It is urgent to understand the coalescence mechanism of crude oil under the action of surfactant solution. This research investigates the coalescence mechanism of numerous oil droplets under liquid flow perturbation. The model was established to study the coalescence process of multiple oil droplets. The effects of the number of oil droplets under homogeneous conditions, the size of oil droplets, and the distance between oil droplets under non-homogeneous conditions on the coalescence process were analyzed. Meanwhile, the change rules of the completion time of oil droplet coalescence were drawn. The results show that the smaller the size of individual oil droplets under non-homogeneous conditions, the longer the coalescence completion time is, and when the size of individual oil droplets reaches the nanometer scale, the time for coalescence of oil droplets is dramatically prolonged. Compared to static circumstances, the time it takes for oil droplets to coalesce is somewhat shorter under gravity. In the fluid flow process, in the laminar flow zone, the coalescence time of oil droplets decreases with the increase of the liquid flow rate. However, in the turbulent flow zone, the coalescence time of oil droplets increases with the increase in the liquid flow rate. The coalescence time is in the range of 600~1000 ms in the flow rate of 0.05~0.2 m/s. In the presence of surfactants, the oil content in the emulsion system increases under the influence of pumping flow. The change in oil content rate with various surfactants is less impacted by flow rate, owing to the stable emulsion structure created by the extracted fluid within the reservoir. The study findings presented in this research provide technical assistance for effective crude oil storage and transportation.

The Interaction and Rheological Behavior of Preformed Gel Particles with Surfactants

Heterogeneous combination flooding systems are composed of surfactant and viscoelastic preformed gel particles (PGP) that can migrate in the pores. The interactions between PGP and surfactants have an important impact on the system. In our research described here heterogeneous aqueous suspensions of PGP and an anionic surfactant (sodium dodecyl benzene sulfonate, SDBS) or a nonionic surfactant coconut oil alkanolamide, (CDEA) were prepared. The effects of SDBS and CDEA on the swelling and rheological properties of the PGP was investigated. In addition, the influence of SDBS and CDEA on the thermal effects of PGP was studied. The results showed that the swelling ratio, apparent viscosity and storage modulus of the PGP decreased with the increase of SDBS concentration. On the contrary, the CDEA increased the swelling ratio and the rheological properties of PGP. The specific heat capacity ( C p ) of PGP increased with the SDBS concentration and decreased with the CDEA concentration. Similar to the rheological behavior, the influence of interactions on the PGP molecular thermal motion was related to the concentration of the surfactants. Therefore, studying the variation of C p with concentration can provide reference for the reasonable dosage of surfactants in heterogeneous oil displacement systems and a new perspective for investigating the interaction between surfactant and PGP or other polymers.

Radiation-Induced Polymerization of Thermo-Responsive Polymers for Enhanced Sweep Efficiency in Heterogeneous Oil Reservoirs

Radiation-Induced Polymerization of Thermo-Responsive Polymers for Enhanced Sweep Efficiency in Heterogeneous Oil Reservoirs

Study on geological reunderstanding and adjustment potential of development zone

Development Zone A is a heterogeneous oil field with continental river-delta deposits. The channel sand body is small in scale and is dominated by straight distributary channel sand body. This type of sand body development scale is small, the drilling rate is low, generally only 17.71%, the plane shows a strong heterogeneity, the well pattern control degree is low, the sand body prediction accuracy under the current well pattern condition is low, generally only about 70%. Due to the small scale of sand body development and the development of faults, the contradiction of imperfect injection-production relationship of single sand body is prominent. In 2016, the expansion Wells of Development Zone A were put into operation successively. Based on the re-understanding of the geology of the new drilling area, the adjustment potential of development Zone A was studied, the injection-production relationship of single sand body was clarified, and the adjustment potential was implemented in combination with the results of multidisciplinary reservoir research, so as to achieve the purpose of improving the water drive control degree of the block and improving the injectionproduction relationship of sand body. After careful analysis, it was preliminatively determined that 19 Wells adjusted for injection and production system were reinjected (6 Wells were reinjected after filling holes). Internal supplementary Wells 19; One oil well was regenerated into a water well, and one water injection well was restored. A total of 41 Wells.

Heterogeneous market participation channels and household welfare

ABSTRACT This paper uses panel data and qualitative interviews from southwestern Ghana to analyse farmers’ heterogeneous oil palm marketing decisions and the effect on household welfare. We show that despite the supposed benefits that smallholders could derive from participation in global agribusiness value chains via formal contracts, such arrangements are rare although two of Ghana’s ‘big four’ industrial oil palm companies are located in the study area. In the absence of formal contracts, farmers self-select into four main oil palm marketing channels (OPMCs). These OPMCs are associated with varying levels of welfare, with processing households and those connected to industrial companies by verbal contracts being better off. Furthermore, own-processing of palm fruits is shown to reduce gender gaps in household welfare. We also unearth community and household level factors that hamper or facilitate participation in remunerative OPMCs. These results have implications for development policy and practice related to inclusive agricultural commercialization.

Sequentially Coupled Thermal-Hydraulic-Mechanical Simulation for Geomechanical Assessments of Caprock Integrity in SAGD

Oil sand reservoirs and caprock undergo deformations triggered by pore pressure increases and thermal induced stresses during the steam-assisted-gravity-drainage (SAGD) processes. Geomechanical assessments are mandated by energy regulators to evaluate the caprock integrity and ensure the safe SAGD operations. Commercial reservoir simulation packages started to incorporate geomechanical effects when predicting flow response; however, these geomechanical modules are not able to correctly model the plastic deformations caused by thermal-hydraulic-mechanical (THM) interactions, which has a first order effect on predicting steam chamber propagation and evaluating caprock integrity.&#x0D; An integrated coupled THM modeling methodology is proposed here to improve the modeling of reservoir deformations and caprock integrity in a heterogeneous oil sand reservoir with interbedded shale barriers. The pressure and temperature front are found to propagate at different speed and that dominate the elastic and plastic deformations caused by changes of shear and mean effective stress. Therefore, four stages are divided in the SAGD process that can be interpretations of changes in stress paths including buildup of pore pressure, generation and dissipation of thermal induced stresses. The response surfaces of minimum factor of safety (FOS) are introduced and computed to provide a conservative estimate for caprock integrity during SAGD of a heterogeneous reservoir with multiple layers of caprocks in Athabasca oil sands. &#x0D;

Well placement optimization with a novel swarm intelligence optimization algorithm: Sparrow Search Algorithm

Well placement optimization plays a critical role in developing oil and gas reservoirs, presenting a formidable challenge due to the high dimension of the search space and computational requirements. This research addresses the simultaneous optimization of well location and flow rates for production and injection wells in a heterogeneous oil reservoir located in southwestern Iran. The objective function employed to evaluate the proposed solutions generated by the Sparrow Search Algorithm (SSA) is the net present value (NPV). To enhance the performance of SSA, a sensitivity analysis is conducted to tune its hyperparameters, while keeping the epochs and population size fixed to manage runtime effectively. The study tackles the challenges posed by reservoir heterogeneity and the time-consuming nature of the optimization process through various strategies. The decision variables are reduced to a reasonable order, and a quality index is introduced to guide the algorithm towards exploring areas of the reservoir with higher potential. Furthermore, runtime is utilized as a termination condition to optimize computational time. The inclusion of the quality map significantly improves the NPV outcomes, allowing for more effective well placement decisions. Physical constraints related to well placement are handled using a penalty method and map cleaning techniques. Having addressed these challenges, the study investigates the impact of fixed parameters on the optimization results. By conducting an optimization run with 100 epochs, comparable outcomes to the case of 50 epochs (with a 5% improvement) are achieved, albeit with a longer runtime of 20 h. However, increasing the population size substantially raises both runtime and computational costs. Therefore, runtime is considered a practical termination condition, and efficient runtimes of 10 and 15 h are selected for this specific problem. The results indicate that a population size of 25 sparrows outperforms the cases with 50 and 100 epochs, respectively, yielding slightly higher NPV values. Furthermore, the performance of SSA is compared to that of Particle Swarm Optimization (PSO) in terms of NPV, convergence, and runtime. The results highlight the advantages of SSA, demonstrating faster convergence and achieving higher NPV values compared to PSO. However, the computational cost of SSA should be taken into consideration, as it requires significantly more time to reach the optimal solution compared to PSO. Overall, this research emphasizes the high potential of the SSA algorithm for optimizing well placements and its practical relevance in real-world oil industry cases.

Optical interpretation of oil emulsions in the ocean - Part III: A three-dimensional unmixing model to quantify oil concentration

Knowledge of oil distribution and characteristics (e.g., oil type, oil thickness, concentration, volume, etc.) is valuable for oil spill response and post-spill assessment. Optical remote sensing has the advantage of collecting information in multiple spectral bands, thus being able to classify and quantify different oil emulsions. However, most spectral characteristics of oil emulsions have been obtained from laboratory measurements, and it is unclear how to apply these lab-based findings to airborne or satellite observations of highly heterogeneous oil patches. This study proposes a three-dimensional (3D) unmixing model to explain AVIRIS image spectra of oil-containing pixels with both horizontal and vertical mixing effects. Based on the 3D unmixing model, an oil concentration lookup table (LUT) and LUT-based oil concentration mapping method are developed to quantify oil concentrations from AVIRIS observations, with the results validated through the use of a spectral similarity index. Using spatial and spectral resampling simulations, we also propose an approach to extend the LUT to different optical sensors (e.g., Landsat) in order to map oil concentrations, yet such an approach requires further research to validate and improve.

Semi-supervised interlayer intelligent recognition method

The development and distribution of interlayers in sandstones can lead to increased heterogeneity in the formation within the reservoir, which further affects the movement of fluids in the formation. Therefore, it is essential to accurately evaluate the interlayers in sandstones, as the precise evaluation of interlayers in sandstones is of great importance for identifying the distribution of underground fluid systems. The logging data of interlayers are inadequate for traditional Machine Learning training due to their low measurement proportion compared to the conventional layers. In logging data, the amount of data in interlayers is significantly smaller than that of conventional reservoirs. Traditional machine learning models are mostly based on samples with balanced distribution. By contrast, semi-supervised learning requires a small number of labeled samples for learning, and then combines a large number of unlabeled samples for modeling. To verify the feasibility of semi-supervised learning in the identification of interlayers, the Donghe sandstone section of the H oilfield was taken as an example. First, the core analysis results were used to label the logging data; then, to uncover more response information that can characterize the interlayers on the logging curve, multiple features were extracted to construct cross features. Finally, an improved model based on autoencoders—probabilistic autoencoder (PAE)—is proposed to address the issue of interlayer recognition for imbalanced samples. The PAE model can calculate the probability of belonging to a different class for unlabeled samples, and classify new samples according to the maximum probability. Experimental results show that, compared with traditional machine learning methods and ensemble learning methods, PAE achieves higher recognition accuracy and better generalization performance by updating the algorithm, and can be used as a simple and fast method for interlayer recognition. The results of the algorithm demonstrate that the semi-supervised method is of great significance for exploring and developing complex heterogeneous oil reservoirs. The research results are of great importance for exploring and developing complex heterogeneous oil reservoirs.

How landscape characteristics in a heterogeneous oil palm plantation mitigate pest abundance: A case study from Mapiripán, Colombia

The oil palm sector is widely known for its strong impact on biodiversity and human well-being. The United Nations Sustainable Development Goals (SDG) framework calls for a sustainable biodiversity management approach to oil palm plantations and thereby balancing palm oil production and biodiversity conservation. Heterogeneous agricultural landscapes, comprising different vegetation types in a complex spatial pattern, provide habitat to a variety of species and support high biodiversity. Biodiversity can thus offer pest control ecosystem services to agricultural systems. However, so far it remains unclear whether landscape structure and diversity foster or mitigate pest occurrences in plantations. Our case study is the oil palm plantation of Mapiripán in Colombia, which integrates local ecosystems such as secondary forests, relict old-grown forests, riparian forests, and water body remnants in between and around the palm stands of the plantation. With this nature-improved design, we ££pose two main questions: which landscape structural properties characterize the landscape of the oil palm plantation?, and how do specific landscape structural properties foster or mitigate the presence of two main oil palm pests (the butterfly larvae split-banded owlet (Opsiphanes cassina) and the red weevil (Rhynchophorus palmarum))? We first characterize the plantation landscape with a multivariate cluster analysis which led us to identify up to nine different landscape structural types for the Macondo plantation. A mosaic of different landscape types ranging from homogeneous and simple landscapes to heterogeneous, diversified, and connected landscape types. Second, we performed an NMDS ordination to show similarities among the landscape structural types, land cover, and pest occurrence. Our results show that the integration of local ecosystems such as forests and water bodies improves landscape connectivity and suppresses the abundance of the two main oil palm pests.

Particle Swamp Optimized -Physics-Informed Deep Learning for Heterogeneous Oil and Gas Underground Reservoir Pressure Management.

In recent years, Particle Swarm Optimization (PSO) has been integrated with machine learning algorithms, such as deep learning, to create powerful hybrid methods that can tackle complex optimization problems more effectively. In the domain of oil and gas reservoir management, underground pressure management is crucial to maximize the yield and efficiency of the reservoir. However, the heterogeneity of the reservoir, along with uncertainties in its properties, makes pressure management a complex and challenging task. To address this issue, researchers have proposed Physics-Informed Deep Learning (PIDL) techniques that incorporate domain-specific knowledge, such as the governing physical equations, into the deep learning framework. Particle Swarm Optimized-Physics-Informed Deep Learning (PSO-PIDL) is a novel hybrid approach that combines PSO with PIDL to optimize the pressure management of heterogeneous oil and gas underground reservoirs. In this approach, the PSO algorithm is used to find the optimal solution for the PIDL-based model that incorporates the governing physical equations of the reservoir. PSO-PIDL can effectively handle the uncertainties and heterogeneity of the reservoir, while also incorporating the physical constraints of the problem. Overall, PSO-PIDL is a promising approach for optimizing the pressure management of oil and gas reservoirs. It can help reduce the operational costs and improve the efficiency of the reservoir, while also ensuring the sustainable use of natural resources. Keywords: Physics-Informed Deep Learning, Particle Swarm Optimization, Bidirectional Long-Short-Term-Memory, Heterogeneous Reservoir, DuPont Finite Element Heat and Mass Transfer Code

Particle Swamp Optimized -Physics-Informed Deep Learning for Heterogeneous Oil and Gas Underground Reservoir Pressure Management.

In recent years, Particle Swarm Optimization (PSO) has been integrated with machine learning algorithms, such as deep learning, to create powerful hybrid methods that can tackle complex optimization problems more effectively. In the domain of oil and gas reservoir management, underground pressure management is crucial to maximize the yield and efficiency of the reservoir. However, the heterogeneity of the reservoir, along with uncertainties in its properties, makes pressure management a complex and challenging task. To address this issue, researchers have proposed Physics-Informed Deep Learning (PIDL) techniques that incorporate domain-specific knowledge, such as the governing physical equations, into the deep learning framework. Particle Swarm Optimized-Physics-Informed Deep Learning (PSO-PIDL) is a novel hybrid approach that combines PSO with PIDL to optimize the pressure management of heterogeneous oil and gas underground reservoirs. In this approach, the PSO algorithm is used to find the optimal solution for the PIDL-based model that incorporates the governing physical equations of the reservoir. PSO-PIDL can effectively handle the uncertainties and heterogeneity of the reservoir, while also incorporating the physical constraints of the problem. Overall, PSO-PIDL is a promising approach for optimizing the pressure management of oil and gas reservoirs. It can help reduce the operational costs and improve the efficiency of the reservoir, while also ensuring the sustainable use of natural resources. Keywords: Physics-Informed Deep Learning, Particle Swarm Optimization, Bidirectional Long-Short-Term-Memory, Heterogeneous Reservoir, DuPont Finite Element Heat and Mass Transfer Code

Seismic Wave Excitation of Mature Oil Reservoirs for Green EOR Technology

Elastic wave-based oil mobilization of residual oil in heterogeneous reservoirs is a viable, low-cost, and green technology method of enhanced oil recovery (EOR). Applications for elastic (seismic) waves at the reservoir scale are currently in the preliminary stages of investigation and development. We employ a two-layer numerical finite element method (FEM) in this research to investigate the possibility of effective propagation of seismic waves in the low permeability area of a mature oil reservoir when seismic stress load is delivered to the rock matrix via a downhole source. The purpose of this research is to evaluate the potential of fluid and rock matrix displacement amplitudes for crossflow generation in a mature oil reservoir. In a low permeability formation, the numerical results reveal that, as the observation radius approaches the reservoir boundary, the rock matrix time-domain displacement performs better as a wave propagation parameter than the pore fluid displacement. However, crossflow oscillations at a peak mesoscopic frequency of 3.0 Hz produce an instantaneous oil transfer rate (recovery rate) of 2.5% (bypassed oil) from the low permeability area. This method can be used in combination with water flooding to recover more oil from both high and low-permeability areas. Mesoscopic attenuation frequency can therefore be utilized as one of the indicators to assess oil recovery in heterogeneous oil reservoirs.

Gas channeling control with an in-situ smart surfactant gel during water-alternating-CO2 enhanced oil recovery

Undesirable gas channeling always occurs along the high-permeability layers in heterogeneous oil reservoirs during water-alternating-CO2 (WAG) flooding, and conventional polymer gels used for blocking the “channeling” paths usually suffer from either low injectivity or poor gelation control. Herein, we for the first time developed an in-situ high-pressure CO2-triggered gel system based on a smart surfactant, N-erucamidopropyl-N,N-dimethylamine (UC22AMPM), which was introduced into the aqueous slugs to control gas channeling in WAG processes. The water-like, low-viscosity UC22AMPM brine solution can be thickened by high-pressure CO2 owing to the formation of wormlike micelles (WLMs), as well as their growth and shear-induced structure buildup under shear flow. The thickening power can be further potentiated by the generation of denser WLMs resulting from either surfactant concentration augmentation or a certain range of heating, and can be impaired via pressurization above the critical pressure of CO2 because of its soaring solvent power. Core flooding tests using heterogeneous cores demonstrated that gas channeling was alleviated by plugging of high-capacity channels due to the in-situ gelation of UC22AMPM slugs upon their reaction with the pre- or post-injected CO2 slugs under shear flow, thereupon driving chase fluids into unrecovered low-permeability areas and producing an 8.0% higher oil recovery factor than the conventional WAG mode. This smart surfactant enabled high injectivity and satisfactory gelation control, attributable to low initial viscosity and the combined properties of one component and CO2-triggered gelation, respectively. This work could provide a guide towards designing gels for reducing CO2 spillover and reinforcing the CO2 sequestration effect during CO2 enhanced oil recovery processes.

The Synergistic Effects between Sulfobetaine and Hydrophobically Modified Polyacrylamide on Properties Related to Enhanced Oil Recovery

In order to explore the mechanism responsible for the interactions in the surfactant-polymer composite flooding and broaden the application range of the binary system in heterogeneous oil reservoirs, in this paper, the influences of different surfactants on the viscosity of two polymers with similar molecular weights, partially hydrolyzed polyacrylamide (HPAM) and hydrophobically modified polyacrylamide (HMPAM), were studied at different reservoir environments. In addition, the relationship between the surfactant-polymer synergistic effects and oil displacement efficiency was also investigated. The experimental results show that for HPAM, surfactants mainly act as an electrolyte to reduce its viscosity. For HMPAM, SDBS and TX-100 will form aggregates with the hydrophobic blocks of polymer molecules, reducing the bulk viscosity. However, zwitterionic surfactant aralkyl substituted alkyl sulfobetaine BSB molecules can build "bridges" between different polymer molecules through hydrogen bonding and electrostatic interaction. After forming aggregates with HMPAM molecules, the viscosity will increase. The presence of two polymers all weakened the surfactant oil-water interfacial membrane strength to a certain extent, but had little effect on the interfacial tension. The synergistic effect of the "bridge" between HMPAM and BSB under macroscopic conditions also occurs in the microscopic pores of the core, which has a beneficial effect on improving oil recovery.

Dynamics of the oil reservoir depletion

In order to study the dynamics of depletion in heterogeneous oil reservoirs on the base of combined finite-element-difference method for the non-stationary problem of piezoconductivity we have carried out a numerical simulation of the pressure distribution in vicinity of the operating well. At that we have taken into account the heterogeneous distribution of filtration characteristics inside the reservoir and the oil infiltration parameters on the boundaries of the reservoir. The developed method for solving the non-stationary problem of piezoconductivity in deformed oil formations allows us adequately to describe the distribution of pressure near production and injection well systems in real operating conditions. We have shown that depletion processes in vicinity of the active well mainly depend on the intensity of oil production and the degree of oil infiltration at the boundaries of the reservoir’s area and to a lesser extent on the filtration parameters inside the reservoir. Therefore, in order to maintain the proper level of oil production in the reservoir’s area, it is necessary, for example, thanks to the use of modern technologies (system of injection wells), to ensure a sufficient inflow of the oil phase at the borders of the considered area. We have shown that in the cases of low oil infiltration at the boundaries of the reservoir area, the value of depletion is directly proportional to the production power of the well. At the same time, a decreasing of the reservoir permeability leads to a slow downing of depletion processes. The limiting value of the oil boundary infiltration coefficient, which allows achieving industrial oil production, is m. At that, the time of reaching of the stationary productive regime is directly proportional to the value of the oil permeability coefficient inside the reservoir. Before installing a system of production and injection wells in heterogeneous oil reservoirs, it is necessary to carry out a systematic analysis of the degree of depletion of the working reservoir’s areas in order to place them in such a way that would ensure the effective dynamics of filtration processes around these areas.

Selective Penetration and Profile Control Performance of Preformed Particle Gels for Heterogeneous Oil Reservoirs.

The preformed particle gel (PPG) has been proved to be an effective chemical agent to reduce fluid channeling and increase the sweeping efficiency. However, we still lack a clear understanding of the field-scale matching relationship between PPG size, elastic modulus and a heterogeneous reservoir. In this respect, the paper carried out various sand pack displacement experiments. The results indicated that an excessively large PPG or elastic modulus would plug a low-permeability sand pack and even increase the severity of fluid channeling. On the contrary, an excessively small PPG or elastic modulus allowed a certain degree of profile control, but the PPG could easily migrate out of high-permeability sand packs with water. If the elastic modulus remained unchanged, the suitable PPG size increased as the reservoir permeability ratio increased. On the other hand, the suitable elastic modulus increased with the increase of the reservoir permeability ratio when the PPG size was kept the same. By using regression analysis, quantitative expressions were established in order to determine the best suitable PPG size for a certain heterogeneous reservoir. When the elastic modulus was fixed, the best suitable PPG mesh exhibited a linear relation with the permeability ratio. This paper provides a useful reference to select the most convenient PPG size and elastic modulus for a potential heterogeneous reservoir, suitable to enhance oil recovery.