Suitable Enhanced Oil Recovery Research Articles

A Systematical Review of the Largest Alkali-Surfactant-Polymer Flood Project in the World: From Laboratory to Pilots and Field Application

Summary This paper presents a systematical review of the largest alkali-surfactant-polymer (ASP) flood project in the world, applied to the largest oil field in China. First, reservoir and fluid characteristics are highlighted. Next, project history is summarized, including laboratory studies, pilot tests, industrial-scale tests, and fieldwide application. Third, typical ASP flooding performance and reservoir management measures from more than 30 years’ experience are presented. In addition, performances of ASP flood and polymer flood in the same field, which is also the largest project in the world, are compared. The Lamadian-Saertu-Xingshugang (La-Sa-Xing) Field in the Daqing Field Complex (including the La-Sa-Xing Field and three smaller satellite fields) is the largest oil field in China. The Upper Cretaceous Saertu-Putaohua-Gaotaizi reservoir has an average porosity of 25% and average permeability of 610 md. The reservoir consists of more than 100 flow units with an average gross and net thickness of 1,377 ft and 394 ft, respectively, and is characterized by significant heterogeneity, both vertically and laterally. The reservoir lies at a depth of 2,566–2,585 ft true vertical depth (TVD), with original reservoir pressure of 1,534–1,740 psi and a reservoir temperature of 113–122°F. Crude oil has an API gravity of 33° and a viscosity of 9 cp at reservoir conditions. The discussed ASP flood project mainly targets high-quality reservoir sands. The field was brought on-stream in 1960 with immediate waterflood. Crossflow and water breakthrough became common issues during water injection, calling for a suitable enhanced oil recovery (EOR) method. The Saertu-Putaohua-Gaotaizi reservoir features favorable conditions for ASP flood, such as temperature, viscosity, permeability, and formation water salinity (7000 mg/L). In addition, the heterogeneous reservoir (permeability variation coefficient of 0.6–0.8) is suitable for ASP flood. ASP flood was studied in the laboratory from 1987 to 1993, followed by five small-scale pilots from 1994 to 1999, all being successful with incremental recoveries of ~20% stock tank oil initially in place (STOIIP). As a result, industrial-scale tests were conducted from 2000 to 2007, resulting in substantial improvement in production from ~4,000 BOPD to greater than 19,000 BOPD. Encouraged by those successes, the ASP project was expanded to fieldwide since December 2007, which is the largest ASP flood project in the industry worldwide. By 2021, daily oil production by ASP flood had reached 96,000 BOPD through 4,825 producers and 4,825 injectors. The actual average incremental recovery factor is 20% over waterflood and 8–10% over polymer flood, resulting in ultimate recovery factor of >60%. Zonal injection and profile modification are effective measures to further improve sweeping efficiency. Scaling is the major challenge during the operation of ASP flood, which is mitigated or remediated by adopting weak alkali ASP, progressive cavity pumps (PCP), scale inhibitor treatment, and fracturing stimulation on damaged wells. As of 2022, oil production by ASP flood is still ongoing at 88,635 BOPD, accounting for 39.9% of total field production. The world’s largest ASP flood project in the La-Sa-Xing Field proved the fieldwide applicability of ASP flood, both technically and economically. The effective reservoir management measures and lessons learned from more than 30 years’ experience provide valuable experiences for large-scale ASP flood projects in the industry.

Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening

One of the main challenges in screening of enhanced oil recovery (EOR) techniques is the class imbalance problem, where the number of different EOR techniques is not equal. This problem hinders the generalization of the data-driven methods used to predict suitable EOR techniques for candidate reservoirs. The main purpose of this paper is to propose a novel approach to overcome the above challenge by taking advantage of the Power-Law Committee Machine (PLCM) technique optimized by Particle Swam Optimization (PSO) to combine the output of five cutting-edge machine learning methods with different types of learning algorithms. The PLCM method has not been used in previous studies for EOR screening. The machine learning models include the Artificial Neural Network (ANN), CatBoost, Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The CatBoost is used for the first time in this work for screening of EOR methods. The role of the PSO is to find the optimal values for the coefficients and exponents of the power-law model. In this study, a bigger dataset than those in previous studies, including 2563 successful worldwide EOR experiences, was gathered. A bigger dataset improves the generalization of the data-driven methods and prevents overfitting. The hyperparameters of the individual machine-learning models were tuned using the fivefold cross-validation technique. The results showed that all the individual methods could predict the suitable EOR method for unseen cases with an average score of 0.868. Among the machine learning models, the KNN and SVM had the highest scores with a value of 0.894 and 0.892, respectively. Nonetheless, after combining the output of the models using the PLCM method, the score of the predictions improved to 0.963, which was a substantial increase. Finally, a feature importance analysis was conducted to find out the most influential parameters on the output. The novelty of this work is having shown the ability of the PLCM technique to construct an accurate model to overcome the class-imbalance issue in EOR screening by utilizing different types of data-driven models. According to feature importance analysis, oil gravity and formation porosity were recognized as the most influential parameters on EOR screening.

Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms

Enhanced oil recovery (EOR) is a complex process which has high investment cost and involves multiple disciplines including reservoir engineering, chemical engineering, geological engineering, etc. Finding the most suitable EOR technique for the candidate reservoir is time consuming and critical for reservoir engineers. The objective of this research is to propose a new methodology to assist engineers to make fast and scientific decisions on the EOR selection process by implementing machine learning algorithms to worldwide EOR projects. First, worldwide EOR project information were collected from oil companies, the extensive literature, and reports. Then, exploratory data analysis methods were employed to reveal the distribution and relationships among different reservoir/fluid parameters. Random forest, artificial neural networks, naïve Bayes, support vector machines, and decision trees were applied to the dataset to establish classification models, and five-fold cross-validation was performed to fully apply the dataset and ensure the performance of the model. Utilizing random search, we optimized the model’s hyper parameters to achieve optimal classification results. The results show that the random forest classification model has the highest accuracy and the accuracy of the test set increased from 88.54% to 91.15% without or with the optimization process, achieving an accuracy improvement of 2.61%. The prediction accuracy in the three categories of thermal flooding, gas injection, and chemical flooding were 100%, 96.51%, and 88.46%, respectively. The results also show that the established RF classification model has good capability to make recommendations of the EOR technique for a new candidate oil reservoir.

EOR screening using optimized artificial neural network by sparrow search algorithm

Enhanced oil recovery (EOR) is a crucial aspect of reservoir engineering, and the use of machine-learning algorithms in the initial stages of screening has been widely accepted as a fast and efficient method for screening the most suitable EOR method. This study presents an artificial neural network (ANN) that recommends the most suitable EOR method based on historical reservoir data. Data from EOR projects worldwide were collected, pre-processed, and then used to build the ANN, which initially achieved a 69% accuracy. The neural network was optimized using the Sparrow Search Algorithm (SSA) and compared with the Particle Swarm Optimization (PSO) algorithm, with a focus on weight and hyperparameter optimization. Validation of the neural network’s prediction was done using recall, precision, and the F1 score. Weight optimization yielded an accuracy of 68% with SSA and 34% with PSO, which were insufficient results for EOR prediction. However, hyperparameter optimization was applied, resulting in an accuracy of 94% with SSA and 90% with PSO. The SSA approach demonstrated faster convergence and higher accuracy in both optimization paths, highlighting its potential for optimizing the neural network in predicting the appropriate EOR method for a given reservoir.

Efforts Lead to Formulation of High-Performance ASP

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 22729, “ASP Formulation Development Journey: Optimization, Validation, and Quality Control for Mangala Field,” by Nitish Koduru, Dhruva Prasad, SPE, and Amitabh Pandey, SPE, Cairn Oil and Gas. The paper has not been peer reviewed. Copyright 2023 International Petroleum Technology Conference. Reproduced by permission. _ Mangala is a large low-salinity, high-quality fluvial oil field in India with stock-tank oil initially in place (STOIIP) of more than 1 billion bbl of waxy and moderately viscous crude. Aqueous-based chemical enhanced oil recovery (EOR) has been identified as the most suitable technique to improve recovery over waterflooding. The objective of the complete paper is to describe alkaline/surfactant/polymer (ASP) formulation development for Mangala, which involved more than 30 corefloods at the time of writing, with formulation design changing over time. Field Background The Mangala field has been regarded as an excellent chemical EOR candidate since its discovery in 2004. The main reservoir unit is divided into the Lower and Upper Fatehgarh formations. These units are subdivided into five reservoir units, FM1 to FM5 from top to bottom. Mangala contains waxy sweet crude oil with gravity ranging from 20 °API near the oil/water contact to 28 °API in the oil column. The crude has in-situ viscosity of 9–17 cp. The crude has good napthenic acid content and reacts with alkali to produce in-situ soap. Immediately after the field’s appraisal stage, work to identify the most suitable EOR process began. First, a polymerflooding pilot was conducted during 2011–12. The encouraging results led to full-field polymerflooding implementation in November 2014. The ASP pilot performed well; incremental oil recovery over polymerflooding was estimated to be in the range of 20–25% of the pilot STOIIP. The initial development plan involved implementing a hot waterflooding production startup to maintain reservoir pressure and effective sweep. The Upper Fatehgarh FM1 and FM2 units were developed with inverted nine-spot patterns, while the more-homogeneous lower Fatehgarh (FM3 and FM4) were developed with downdip injectors and updip horizontal producers. The Mangala field began producing under a hot waterflood in 2009, with the injected water heated to approximately 80–85°C to avoid wax deposition. Injection was started within a few months of first production. At the time of writing, the field is under polymerflooding, with results largely in line with expectations. The field, which has already produced approximately 37% of the STOIIP, is on a declining production trend, and large-scale ASP flooding is being considered as the next development initiative to arrest production decline and increase overall recovery. The complete paper discusses EOR screening studies; ASP formulations, including the initial formulation; the method adopted for selection of chemical suppliers for the pilot test; optimization of ASP formulations tested in the pilot; validation of the developed formulation to other layers of the field; and planned quality-control processes to be used during procurement of chemicals for larger applications of ASP floods in the field. This synopsis concentrates on the last three of these factors, rather than the formulations considered, which, in addition to the initial formulation, included the following: - Formula 2: ASP formulation with a sacrificial surfactant - Formula 3: ASP formulation with a single surfactant - Formula 4: Formulation with a highly hydrophilic, large-hydrophobe surfactant

Supervised deep learning-based paradigm to screen the enhanced oil recovery scenarios

High oil prices and concern about limited oil reserves lead to increase interest in enhanced oil recovery (EOR). Selecting the most efficient development plan is of high interest to optimize economic cost. Hence, the main objective of this study is to construct a novel deep-learning classifier to select the best EOR method based on the reservoir’s rock and fluid properties (depth, porosity, permeability, gravity, viscosity), and temperature. Our deep learning-based classifier consists of a one-dimensional (1D) convolutional neural network, long short-term memory (LSTM), and densely connected neural network layers. The genetic algorithm has been applied to tune the hyperparameters of this hybrid classifier. The proposed classifier is developed and tested using 735 EOR projects on sandstone, unconsolidated sandstone, carbonate, and conglomerate reservoirs in more than 17 countries. Both the numerical and graphical investigations approve that the structure-tuned deep learning classifier is a reliable tool to screen the EOR scenarios and select the best one. The designed model correctly classifies training, validation, and testing examples with an accuracy of 96.82%, 84.31%, and 82.61%, respectively. It means that only 30 out of 735 available EOR projects are incorrectly identified by the proposed deep learning classifier. The model also demonstrates a small categorical cross-entropy of 0.1548 for the classification of the involved enhanced oil recovery techniques. Such a powerful classifier is required to select the most suitable EOR candidate for a given oil reservoir with limited field information.

Accurate prediction of enhanced oil recovery from carbonate reservoir through smart injection of nanoparticle and biosurfactant

Selecting the most suitable enhanced oil recovery (EOR) method and subsequently an accurate prediction of EOR are essential in reservoir engineering. Generally, interfacial tension (IFT) and wettability alteration are effective agents on EOR. The aim of this work is to understand how contact angle can be changed additionally to the IFT changes. Different tests on IFT, wettability alteration, and core flooding of limestone slabs using rhamnolipid, N-doped graphene nanoparticle, and a mixture of aqueous solution of rhamnolipid with N-doped graphene were performed. Rhamnolipid at the CMC (250 mg/l) had just a significant effect on IFT reduction so that IFT decreased from 8.1(brine/oil system) to 0.4 mN/m. N-doped graphene at the CMC (500 mg/l), had only a significant effect on wettability so that it altered the slabs wettability from strongly oil wet (θ = 133.5°) toward strongly water-wet (θ = 51.5°). When both were used simultaneously at their CMC, the contact angle and IFT reached to 56° and 0.97 mN/m, respectively. Based on core flooding tests, 8.2%, 18.1%, and 24.2% of OOIP were obtained using N-doped graphene, rhamnolipid, and a mixture of them at their CMC, respectively. Our results showed that IFT has a more important role on Sor than the wettability change. The findings of this study can help for better understanding of the mechanism behind EOR using the combination of the biosurfactants and nanoparticles.

Development of an evolutionary artificial neural network-based tool for selecting suitable enhanced oil recovery methods

Development of an evolutionary artificial neural network-based tool for selecting suitable enhanced oil recovery methods

Screening selection of enhanced oil recovery methods based on analytics of worldwide oilfield data with reference to offshore oil fields in Vietnam

Selecting a proper enhanced oil recovery (EOR) method for a prospective reservoir is a key factor for successful application of EOR techniques. Reservoir engineers usually refer to screening guidelines to identify potential EOR processes for a given reservoir. However, these guidelines are often too general. In this study, we develop an advanced EOR screening technique based on the statistical analyses with boxplot in combination with some initial deep learning analyses to select the most suitable EOR method for a given mature oil field. At first, a database and the screening guidelines were established by compiling the information of 1,098 EOR projects from various publications in different languages, including Oil and Gas Journal (OGJ) biannual EOR surveys, SPE publications, DOE reports, and Chinese publications, etc. Boxplots were used to detect the special cases for each reservoir/fluid property and to present the graphical screening results. A case study was used to demonstrate that with a simple input of reservoir/fluid information, the proposed procedure could effectively give recommendations for EOR method selection. With the inputs (reservoir and fluid properties) from Vietnam offshore oil fields, the EOR methods recommended by this study are mostly chemical, including polymer and surfactant injection.

Numerical Simulation of Low Salinity Water Flooding on Core Samples for an Oil Reservoir in the Nam Con Son Basin, Vietnam

Low-salinity water flooding (LSWF) is environment-friendly and operates similarly to conventional waterflooding without the need for synthetic chemical materials. The application of LSWF makes sense in Vietnam as HC production has steadily declined since 2002, and the majority of main oil fields have become near mature and mature fields. In the next years, Enhanced Oil Recovery (EOR) should be a top priority for Petro Vietnam to boost its oil production, for which the key issue is how to select a suitable EOR technology. In this study, LSWF of the Lower Miocene sand using low salinity water from Lower Oligocene sand was investigated. Previously at the Ruby field in the Cuu Long Basin, an LSWF feasibility study was carried out based on a conventional core flooding experiment, which is time-consuming and costly. This study targets the Chim Sao field in the Nam Con Son Basin, for which a cheaper and faster assessing method is required. As a result, a numerical code written in Matlab was developed and successfully validated with the core flooding experiment results obtained at the Ruby field. The LSWF simulation was conducted using the multiple ion-exchange mechanisms (MIE), and the results obtained showed an increase in the oil recovery factor by 2.19% for the Lower Miocene Sand. Another important outcome of this study is the innovative proposal and successful simulation to use the abundant low salinity water from the underlying Lower Oligocene sand as a natural LSW source to inject into the Lower Miocene oil reservoir that can be a decisive factor to help apply LSWF in practice on a wide scale not only for Chim Sao but also other similar oil fields in southern offshore Vietnam.

Application of machine learning techniques for selecting the most suitable enhanced oil recovery method; challenges and opportunities

A reliable method for selecting the most suitable Enhanced Oil Recovery (EOR) method is of a high necessity in reservoir engineering. Machine Learning (ML) techniques have been widely adopted as an effective and fast approach in primary stage of EOR screening. This paper presents a step-by-step procedure of applying ML in EOR screening with focusing on two main goals. Firstly, the problem of EOR screening is investigated from ML point of view. A large database consisting of more than 1000 experiences of worldwide EOR projects has been gathered from various sources. On the basis of this database, several ML models have been developed for predicting the suitable category of EOR methods for a candidate reservoir. These models include: Shallow and Deep Artificial Neural Networks (ANN), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF) and Dimension Reduction using Principal Component Analysis (PCA). Almost all models performed well in predicting non-observed data with an average accuracy ranging from 80 to 90%. However, RF and the deep ANN models delivered the best performance with an average accuracy of 90%. The results demonstrated that RF model performed much better in comparison with the DT model and improved the classification accuracy from 85 to 91%. In case of ANNs, it was observed that the deeper ANN developed here, improved the classification accuracy from 83 to 90% in comparison with the shallow ANN with one hidden layer. The weakest performance was delivered by NB classifier with an average accuracy of 49%. As the second purpose of this research, reliability of screening results from applying ML techniques is discussed in details. Because, this approach suffers from some serious limitations and challenges, which could unfavorably affect the screening results and never had been considered in previous works. In this work, potential challenges and limitations associated with this well-known approach are introduced from database issues to modeling problems. Accordingly, an important conclusion is that application of ML, although providing a very good insight into primary EOR screening, should not be relied upon as the only method for predictions. It is recommended that the obtained results from this approach should be cross-checked with other EOR screening approaches or with methods based on expert's knowledge.

Screening of Enhanced Oil Recovery Methods in One of Iran’s Offshore Oil Fields

Enhanced oil recovery (EOR) is a vital part of the process of oil production from sandstone and carbonate reservoirs. Maintaining and increasing oil production from many fields require proper selection, design, and implementation of EOR methods. The selection of EOR methods for specific reservoir conditions is one of the most difficult tasks for oil and gas companies. Screening of different EOR techniques considering previous experiences from the methods applied in other fields is a first step in the recommendation of any costly EOR operations. In this paper, EORgui software was utilized to screen eight enhanced oil recovery methods in one of Iran’s offshore sandstone oil fields. The reservoir is composed of two sections with different fluid properties, namely API, viscosity, and oil composition, but relatively homogeneous rock properties and high permeability (1500 mD). The results show that polymer flooding is technically the most suitable enhanced oil recovery method in the upper zone of the reservoir with a high percentage matching score of 90%, and immiscible gas injection with a matching score of 83% is ranked second. For the lower part of the reservoir containing a fluid with much higher viscosity, immiscible gas injection (83% matching) can be recommended. Furthermore, polymer flooding predictive module (PFPM) was utilized to investigate the impact of polymer concentration on oil recovery performance of the upper part with an ultimate recovery of about 40% at the optimum concentration.

Mathematical Model of Water Alternated Polymer Injection

Chemical enhanced oil recovery (EOR) methods include the injection of aqueous polymer solutions slugs driven by water. Polymer solutions increase water viscosity, decreasing the water phase mobility and improving oil recovery through better sweep efficiency. In this paper, we present the water alternated polymer EOR technique, which is based on the injection of successive polymer slugs alternated by water slugs. The mathematical problem is composed by two conservation equations: one of them is related to the water volume and the other one to the polymer mass. We assume that the polymer may be adsorbed by the rock, and the relation between the concentration in the aqueous solution and the solid is governed by a Langmuir type adsorption isotherm. The water viscosity is a function of the polymer concentration in water. The 2 × 2 system of hyperbolic equations was decoupled by introducing a potential function instead of time as an independent variable. The water alternated polymer injection is represented by a varying boundary condition. The analytical solution presents interactions between waves of different families. It is shown that the polymer slugs always catch up each other along the porous media generating a single slug. As a consequence, the water slugs will disappear. This solution is new and was compared to numerical results with close agreement. It also can be used for the selection of the most suitable enhanced oil recovery technique for a particular oil field.

Rheological analysis and EOR potential of surfactant treated single-step silica nanofluid at high temperature and salinity

Single-step nanofluids have shown better stability and size control properties than conventional two-step nanofluids under similar conditions. However, for wide-spread oilfield acceptance, there is a need to investigate the role of oilfield conditions viz., temperature, salt and pH on stability of single-step nanofluids. Thus, in this study, single-step silica nanofluids were synthesized in base fluid (1000 ppm polyacrylamide, PAM) and their stability was investigated using methods such as dynamic light scattering (DLS) and rheological analysis to establish their viability as suitable enhanced oil recovery (EOR) agents. PAM is a widely used oilfield practically applicable polymer. Primarily, the single-step silica nanofluids were found to destabilize at higher temperatures (≥90 °C) due to thermal stability issues of PAM at high temperatures. This was also understood by PAM rheology. The inclusion of salt reduced the stability of nanofluids due to the suppression of inter-particle repulsive forces. The variation in pH also influenced nanofluid stability and pH range of 8–10 was observed for the stable behavior of nanofluids. The addition of an anionic surfactant (SDS) improved nanofluid stability by reducing salt-induced NP agglomeration; increasing surfactant concentration initially improved nanofluid resistance to salt induced agglomeration and at high surfactant concentrations, the nanofluids exhibited greater NP agglomeration even in the absence of salt. The oil recovery results also showed that surfactant use in the synthesis of silica nanofluid for oil recovery applications may provide better results than sole silica nanofluid. Based on the observations, surfactant treated single-step silica nanofluid use was found favorable for oilfield practices where conventional nanofluids may show challenges.

Screening Reservoir Candidates for Enhanced Oil Recovery (EOR) in Angolan Offshore Projects

The neuro-fuzzy (NF) approach presented in this work is based on five (5) layered feedforward backpropagation algorithm applied for technical screening of enhanced oil recovery (EOR) methods. Associated reservoir rock-fluid oilfield data from successful EOR projects were used as input and predicted output in the training and validation processes, respectively. The developed model was then tested by using data set from Block B of an Angolan oilfield. The results of the sensitivity analysis between the Mamdani and the Takagi-Sugeno-Kang (TSK) approach incorporated in the algorithm has shown the robustness of the TSK ANFIS (Adaptive Neuro-Fuzzy Inference System) approach in comparison to the other approach for the prediction of a suitable EOR technique. The simulation test results showed that the model presented in this study can be used for technical selection of suitable EOR techniques. Within the area investigated (Block B, Angola) polymer, hydrocarbon gas, and combustion were identified as the suitable techniques for EOR.

Experimental study of injection strategy for Low-Tension-Gas flooding in low permeability, high salinity carbonate reservoirs

Previous experimental studies have proven that Low-Tension-Gas (LTG) flooding can be a suitable enhanced oil recovery (EOR) method for low-permeability carbonate reservoirs with high salinity and hard formation brine. LTG flooding was observed to improve oil recovery by combining two effects: a reduction of the interfacial tension (IFT) between oil and water and mobility control through the formation of in-situ foam with an injected gas. However, the high cost of chemicals and/or the limited supply of gas could make this process economically challenging.In this study, the primary goal was to reduce the amount of injected gas and surfactant to make the LTG process more economically feasible. A low-permeable (<10 mD) limestone reservoir with a high formation brine salinity (>200,000 ppm and hardness 19,000 ppm) and temperature 69 °C was the target reservoir of this study. Effect of varying the concentration and pore volumes of the ultra-low IFT inducing surfactant slug injected on oil recovery was studied. Nitrogen gas was co-injected during selected time periods throughout the entire surfactant injection in order to identify the significance of mobility control during the crucial phases of the LTG flooding. The coreflood results emphasized the significance of the injection of gas, even at lower foam quality, for the maintenance of mobility control. Ultimate oil recovery of over 60% (residual oil post waterflood) was achieved, even after reducing the surfactant concentration by 75% and inducing a different in-situ salinity profile as compared to earlier studies. An innovative method for measuring surfactant adsorption using Liquid Chromatography and Mass Spectrometry (LC-MS) was developed, which could provide individual surfactant transport data for each of the three classes of surfactants used.

Molecular Simulation of Enhanced Oil Recovery in Shale

Enhanced oil recovery (EOR) techniques (miscible/near miscible gas injection, chemical flooding and thermal) aim at increasing oil recovery factor, over which that would be achieved from natural depletion and pressure maintenance methods. As we embark on more complex EOR processes (e.g. miscible hydrocarbon/non-hydrocarbon, surfactant/polymer, smart water and combination of the three), complex phase equilibria, complicated rock/fluid interaction, and sophisticated transport through porous media will create a number of key technical challenges that must be addressed. The issue is significantly increased in unconventional resources as the physics behind the process is not very well understood.For these resources, effective enhanced oil recovery (EOR) techniques are required to displace oil from nanoscale shale matrix. Due to small permeability, it is difficult to conduct water and chemical flooding in these resources. Maintaining a stable flood front in immiscible gas flooding due to the severe fingering phenomenon in fractured shale formations. Gas huff-n-puff becomes the most suitable EOR method in shale reservoir development. For decades, CO2 (EOR) techniques that have been successfully applied in conventional reservoirs to improve oil production. In this work, we will investigate the physics behind CO2 injection into organic nanopores of shale using molecular dynamics simulations. A 3D kerogen structure is used with dodecane to study the huff-n-puff process at a molecular level. Results show that there is an optimal soaking time after which the recovery factor is not affected by soaking time anymore. Furthermore, carbon dioxide has high affinity to be adsorbed to kerogen walls and therefore desorbing the hydrocarbon molecules.

The application of nanofluids for recovery of asphaltenic oil

ABSTRACTIn the recent years, requirement of suitable enhanced oil recovery (EOR) technique as a more proficient technology becomes significant because of increasing demand for energy. Nanofluids have great potential in order to improve oil recovery. In our study, the effect of SiO2, Al2O3, and MgO nanoparticles on oil recovery was investigated by using core flooding apparatus. Zeta potential and particle size distribution measurements were carried out to investigate the stability of nano particles and results showed SiO2 has more stability than other ones. Interfacial tension and contact angle measurements between nanofluids and crude oil used to demonstrate that how nanoparticles enhance oil recovery. Experimental data reveals that SiO2 nanoparticles introduce as the greatest agent among these nanoparticles for enhanced oil recovery. Lowest damage for SiO2 nanofluids was observed and also it was observed that the concentration and injection rate have straight effects on permeability reductions.

Neuro-Simulation Tool for Enhanced Oil Recovery Screening and Reservoir Performance Prediction

Assessment of the suitable enhanced oil recovery method in an oilfield is one of the decisions which are made prior to the natural drive production mechanism. In some cases, having in-depth knowledge about reservoir’s rock, fluid properties, and equipment is needed as well as economic evaluation. Both putting such data into simulation and its related consequent processes are generally very time consuming and costly. In order to reduce study cases, an appropriate tool is required for primary screening prior to any operations being performed, to which leads reduction of time in design of ether pilot section or production under field condition. In this research, two different and useful screening tools are presented through a graphical user interface. The output of just over 900 simulations and verified screening criteria tables were employed to design the mentioned tools. Moreover, by means of gathered data and development of artificial neural networks, two dissimilar screening tools for proper assessment of suitable enhanced oil recovery method were finally introduced. The first tool is about the screening of enhanced oil recovery process based on published tables/charts and the second one which is Neuro-Simulation tool, concerns economical evaluation of miscible and immiscible injection of carbon dioxide, nitrogen and natural gas into the reservoir. Both of designed tools are provided in the form of a graphical user interface by which the user, can perceive suitable method through plot of oil recovery graph during 20 years of production, costs of gas injection per produced barrel, cumulative oil production, and finally, design the most efficient scenario.

Data Analysis and Neuro-Fuzzy Technique for EOR Screening: Application in Angolan Oilfields

In this work, a neuro-fuzzy (NF) simulation study was conducted in order to screen candidate reservoirs for enhanced oil recovery (EOR) projects in Angolan oilfields. First, a knowledge pattern is extracted by combining both the searching potential of fuzzy-logic (FL) and the learning capability of neural network (NN) to make a priori decisions. The extracted knowledge pattern is validated against rock and fluid data trained from successful EOR projects around the world. Then, data from Block K offshore Angolan oilfields are then mined and analysed using box-plot technique for the investigation of the degree of suitability for EOR projects. The trained and validated model is then tested on the Angolan field data (Block K) where EOR application is yet to be fully established. The results from the NF simulation technique applied in this investigation show that polymer, hydrocarbon gas, and combustion are the suitable EOR techniques.