Field Data and Laboratory Study to Investigate Water and Gas Injectivity and Cyclic Hysteresis in Miscible WAG Injection in Carbonate Reservoir

2021 ◽  
Author(s):  
Shehadeh Masalmeh ◽  
Aaesha Al-Keebali ◽  
Arit Igogo
Keyword(s):  
Field Data ◽  
Water Saturation ◽  
Carbonate Reservoir ◽  
Carbonate Reservoirs ◽  
Experimental Program ◽  
Water Mobility ◽  
Three Phase ◽  
Reservoir Conditions ◽  
Cycle Dependent ◽  
Better Than

Abstract The objective of this paper is to investigate the water and gas injectivity in water alternating gas (WAG) projects using laboratory and field scale data. It has been reported in the literature that both gas and water mobility has been significantly reduced in three-phase flow compared to two-phase flow. This behaviour has been attributed to a cycle dependent hysteresis effect which reduced both gas and water mobility in the different injection cycles. To address the gas and water injectivity and the cycle dependent hysteresis concept, the results of a detailed experimental program in addition to field injectivity data will be presented. The experimental program included three-phase experiments performed under reservoir conditions using live crude oil and carbonate reservoir core material. The core wettability was restored by ageing the core in crude oil for several weeks under reservoir conditions and CO2 was used as miscible injectant. The field injectivity data is obtained from two CO2 WAG pilots in a carbonate reservoir. The main conclusions of the study are: 1- Gas injectivity in the presence of mobile water is much lower than that in the absence of water, 2- Water injectivity in experiments starting with water cycle is better than that in experiments starting with gas cycle when compared at the same water saturation, 3- Cyclic hysteresis in gas relative permeability was observed when comparing the first and second gas cycle, however, no further hysteresis was observed in the subsequent gas injection cycles, 4- Cyclic hysteresis in water relative permeability was not observed, the injectivity was either the same or higher in the subsequent cycles. 5- The gas injectivity at similar gas saturation for experiments starting with gas is better than that for experiments starting with water, 6- Gas and water injectivity field data from ongoing CO2 WAG projects in carbonate reservoirs showed no cyclic hysteresis, the injectivity either the same or improved in the subsequent cycles, 7- The CO2 injectivity was lower than expected, in the same order as water injectivity, which could be due to injecting CO2 in high water saturation zone and 8) The low CO2 injectivity could have a positive impact on sweep efficiency and potential improvement of oil recovery. This paper presents the results of a well-designed experimental program and field data from two CO2 WAG pilots to systematically investigate water and gas injectivity in miscible WAG projects in carbonate reservoirs. The paper provides a rich and rarely available set of experimental and field data that can help improve and optimize gas and WAG injection projects in carbonate reservoirs. Detailed analysis of the field gas and water injectivity data will be presented and discussed in-light of the laboratory experimental data.

Petrofacies classification using machine learning algorithms

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA101-WA113 ◽  
Author(s):  
Adrielle A. Silva ◽  
Mônica W. Tavares ◽  
Abel Carrasquilla ◽  
Roseane Misságia ◽  
Marco Ceia
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Water Saturation ◽  
Carbonate Reservoir ◽  
Machine Learning Algorithms ◽  
Carbonate Reservoirs ◽  
Training Data ◽  
Southeastern Brazil ◽  
Gradient Boosting ◽  
Data Set

Carbonate reservoirs represent a large portion of the world’s oil and gas reserves, exhibiting specific characteristics that pose complex challenges to the reservoirs’ characterization, production, and management. Therefore, the evaluation of the relationships between the key parameters, such as porosity, permeability, water saturation, and pore size distribution, is a complex task considering only well-log data, due to the geologic heterogeneity. Hence, the petrophysical parameters are the key to assess the original composition and postsedimentological aspects of the carbonate reservoirs. The concept of reservoir petrofacies was proposed as a tool for the characterization and prediction of the reservoir quality as it combines primary textural analysis with laboratory measurements of porosity, permeability, capillary pressure, photomicrograph descriptions, and other techniques, which contributes to understanding the postdiagenetic events. We have adopted a workflow to petrofacies classification of a carbonate reservoir from the Campos Basin in southeastern Brazil, using the following machine learning methods: decision tree, random forest, gradient boosting, K-nearest neighbors, and naïve Bayes. The data set comprised 1477 wireline data from two wells (A3 and A10) that had petrofacies classes already assigned based on core descriptions. It was divided into two subsets, one for training and one for testing the capability of the trained models to assign petrofacies. The supervised-learning models have used labeled training data to learn the relationships between the input measurements and the petrofacies to be assigned. Additionally, we have developed a comparison of the models’ performance using the testing set according to accuracy, precision, recall, and F1-score evaluation metrics. Our approach has proved to be a valuable ally in petrofacies classification, especially for analyzing a well-logging database with no prior petrophysical information.

scholarly journals A Visual Investigation of CO2 Convective Mixing in Water and Oil at the Pore Scale Using a Micromodel Apparatus at Reservoir Conditions

Gases ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 53-67
Author(s):  
Widuramina Amarasinghe ◽  
Seyed Farzaneh ◽  
Ingebret Fjelde ◽  
Mehran Sohrabi ◽  
Ying Guo
Keyword(s):  
Water Saturation ◽  
Multiphase System ◽  
Phase System ◽  
Pore Scale ◽  
Convective Mixing ◽  
Co2 Transport ◽  
Dead End ◽  
Three Phase ◽  
Reservoir Conditions ◽  
First Time

CO2 convective mixing in water has been visualized in Hele-Shaw and PVT cell experiments but not at the pore scale. Furthermore, CO2 convective mixing in a three-phase system (i.e., CO2 in the presence of both water and oil) has not been visually investigated. A vertically placed micromodel setup was used to visualize CO2 convective mixing at 100 bar and 50 °C, representative of reservoir conditions. To the best of our knowledge, for the first time, we have visually investigated CO2 convective mixing in water at the pore scale and also CO2 convective mixing in a multiphase system (water and oil). CO2 mixing in water governed by both diffusion and convection mechanisms was observed. The vertical CO2 transport velocity was calculated to be 0.3 mm/min in both a 100% water saturation system and a residual oil-saturated system. First, CO2 always found the easiest path through the connected pores, and then CO2 was transported into less connected pores and dead-end pores. CO2 transport into dead-end pores was slower than through the preferential path. CO2 transport into water-filled ganglia with trapped oil was observed and was slower than in water.

Nanoparticles Screening Criteria for Wettability Alteration in Carbonate Reservoirs

2021 ◽  
Author(s):  
Xiao Deng ◽  
Muhammad Shahzad Kamal ◽  
Shirish Patil ◽  
Muhammad Shakil Hussain
Keyword(s):  
Carbonate Rock ◽  
Carbonate Reservoir ◽  
Carbonate Reservoirs ◽  
Titania Nanoparticles ◽  
Wettability Alteration ◽  
Divalent Ions ◽  
Rock Samples ◽  
Low Salinity ◽  
Monovalent Ions ◽  
Reservoir Conditions

Abstract Wettability alteration has been recognized as a key mechanism for enhanced oil recovery in oil-wet carbonate reservoirs. Nanoparticles can change rock surfaces from oil-wet to water-wet. However, there are multiple kinds of nanoparticles and different reservoir conditions. It is necessary to establish a screen system to guide nanoparticles selection. Temperature and salinity are two important reservoir conditions that may have a big influence on the performance of nanoparticles. Besides, divalent ions and monovalent ions can have a different impact. In this paper, different kinds of commercial nanoparticles were used as wettability modifiers on carbonate rock. Metallic oxide nanoparticles, SiO2, and titania nanoparticles were dissolved into brine solution under varying temperatures (50°C – 100°C) and salinity (2500 – 210,000ppm). Carbonate rock samples were aged by crude oil to make them oil-wet. After that, rock samples were immersed in brine and nanoparticle solutions to measure the contact angles under different temperature. Rock surfaces were then examined by scanning electron microscope (SEM) to see the adsorption and distribution of nanoparticles. Stability data shows that nanoparticles tend to be more stable under high temperature, low salinity conditions. Besides, monovalent ions have a smaller impact on nanoparticle stability than divalent ions. By monitoring contact angle with time, a quicker wettability alteration was observed with SiO2 and CaCO3 nanoparticles on carbonate rock under higher temperatures, indicating that temperature has a significant influence on the performance of nanoparticles. SEM images show that under low salinity conditions, nanoparticles tend to be more scattered. This paper showed that nanoparticles are very effective materials for carbonate wettability alteration under varying temperature and salinity. Besides, the relative performance of nanoparticles under different conditions and a screening guide according to carbonate reservoir conditions are provided

A Novel Deep Reinforcement Sensor Placement Method for Waterfront Tracking

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Abdallah Al Shehri ◽  
Alberto Marsala
Keyword(s):  
Reinforcement Learning ◽  
Water Saturation ◽  
Learning Algorithm ◽  
Sensor Placement ◽  
Base Station ◽  
Carbonate Reservoir ◽  
Carbonate Reservoirs ◽  
Sensor Data ◽  
Sweep Efficiency ◽  
Reservoir Monitoring

Abstract Waterfront movement in fractured carbonate reservoirs occurs in micro-fractures, corridors and interconnected fracture channels (above 5 mm in size) that penetrate the carbonate reservoir structure. Determining the fracture channels and the waterfront movements within the flow corridors is critical to optimize sweep efficiency and increase hydrocarbon recovery. In this work, we present a new deep reinforcement learning algorithm for the optimization of sensor placement for waterfront movement detection in carbonate fracture channels. The framework deploys deep reinforcement learning approach for optimizing the location of sensors within the fracture channels to enhance waterfront tracking. The approach first deploys the deep learning algorithm for determining the water saturation levels within the fractures based on the sensor data.. Then, it updates the sensor locations in order to optimize the reservoir coverage. We test the deep reinforcement learning framework on a synthetic fracture carbonate reservoir box model exhibiting a complex fracture system. Fracture Robots (FracBots, around 5 mm in size) technology will be used to sense key reservoir parameters (e.g., temperature, pressure, pH and other chemical parameters). The technology is comprised of a wireless micro-sensor network for mapping and monitoring fractures in conventional and unconventional reservoirs [1]. It establish a wireless network connectivity via magnetic induction (MI)-based communication since it exhibits highly reliable and constant channel conditions with sufficient communication range in the oil reservoir environment. The system architecture of the FracBots network has two layers: FracBot nodes layer and a base station layer. A number of subsurface FracBot sensors are injected in the formation fractures that record data affected by changes in water saturation. The sensor placement can be adapted in the reservoir formation to improve sensor data quality, as well as better track the penetrating waterfronts. They will move with the injected fluids and distribute themselves in the fractures where they start sensing the surrounding environment's conditions and communicate data, including their location coordinates, among each other to finally send the information in multi-hop fashion to the base station installed inside the wellbore. The base station layer consists of a large antenna connected to an aboveground gateway. The data collected from the FracBots network will be transmitted to the control room via aboveground gateway for further processing. The results exhibited resilient performance in updating the sensor placement to capture the penetrating waterfronts in the formation. The framework performs well particularly when the distance between the sensors is sufficient to avoid measurement interference. The framework demonstrates the criticality of adequate sensor placement in the reservoir formation for accurate waterfront tracking. Also, it shows that itis a viable solution to optimize sensor placement for reservoir monitoring. This novel framework presents a vital component in the data analysis and interpretation of subsurface reservoir monitoring system for carbonate reservoirs. The results outline the opportunity to deploy advanced artificial intelligence algorithms, such as deep reinforcement methods, to optimally place downhole sensors to achieve best measurement success, and track the waterfronts as well as determine sweep efficiency.

scholarly journals Cementation exponent estimate in carbonate reservoirs: A new method

2020 ◽  
Vol 10 (2) ◽  
pp. 66-72
Author(s):  
Adel Shirazy ◽  
Keyvan Khayer ◽  
Aref Shirazi ◽  
Abdolhamid Ansari ◽  
Ardeshir Hezarkhani
Keyword(s):  
Water Saturation ◽  
Carbonate Reservoir ◽  
Carbonate Reservoirs ◽  
New Method ◽  
Matrix Density ◽  
Close Relationship ◽  
The Matrix ◽  
Log Interpretation ◽  
Fundamental Equations ◽  
Cementation Factor

There are two approaches for measuring hydrocarbon saturation: well log interpretation and usually developed formulas. Archie’s equation is one of the most fundamental equations used for water saturation calculation. Archie’s equation includes three factors: cementation factor, tortuosity and saturation exponent. Archie determines these factors based on lab results in sandstone and provides fixed value for them. Carbonate reservoirs have a variety of textures, shapes and distribution of pores; therefore, the mentioned factors, especially cementation are not considered constant. In this study, the relationship between cementation factor and density log was examined because cementation factor is defined as a parameter that has a close relationship with density. By calculating the matrix density and accordance factor between the matrix density and cementation factor from core’s analysis, a log will be generated that can estimate the variation of cementation factor around the borehole. This method is useable for calculating the cementation factor in carbonate rocks.   Keywords: Cementation factor, carbonate reservoir, density, new method, exponents.

scholarly journals Injection-production optimization of carbonate reservoir based on an inter-well connectivity model

2021 ◽  
pp. 014459872199465
Author(s):  
Yuhui Zhou ◽  
Sheng Lei ◽  
Xuebiao Du ◽  
Shichang Ju ◽  
Wei Li
Keyword(s):  
History Matching ◽  
Water Injection ◽  
Field Application ◽  
Carbonate Reservoir ◽  
Production Optimization ◽  
Oil Field ◽  
Carbonate Reservoirs ◽  
Utilization Rate ◽  
Production Volume ◽  
Connectivity Model

Carbonate reservoirs are highly heterogeneous. During waterflooding stage, the channeling phenomenon of displacing fluid in high-permeability layers easily leads to early water breakthrough and high water-cut with low recovery rate. To quantitatively characterize the inter-well connectivity parameters (including conductivity and connected volume), we developed an inter-well connectivity model based on the principle of inter-well connectivity and the geological data and development performance of carbonate reservoirs. Thus, the planar water injection allocation factors and water injection utilization rate of different layers can be obtained. In addition, when the proposed model is integrated with automatic history matching method and production optimization algorithm, the real-time oil and water production can be optimized and predicted. Field application demonstrates that adjusting injection parameters based on the model outputs results in a 1.5% increase in annual oil production, which offers significant guidance for the efficient development of similar oil reservoirs. In this study, the connectivity method was applied to multi-layer real reservoirs for the first time, and the injection and production volume of injection-production wells were repeatedly updated based on multiple iterations of water injection efficiency. The correctness of the method was verified by conceptual calculations and then applied to real reservoirs. So that the oil field can increase production in a short time, and has good application value.

Simulation of Miscible Displacement in Full-Diameter Carbonate Cores

1982 ◽  
Vol 22 (05) ◽  
pp. 647-657 ◽  
Author(s):  
J.P. Batycky ◽  
B.B. Maini ◽  
D.B. Fisher
Keyword(s):  
Oil Recovery ◽  
Water Saturation ◽  
Accurate Determination ◽  
Miscible Displacement ◽  
Carbonate Reservoir ◽  
Core Material ◽  
Immiscible Fluid ◽  
Displacement Efficiency ◽  
Carbonate Cores ◽  
Capacitance Model

Abstract Miscible gas displacement data obtained from full-diameter carbonate reservoir cores have been fitted to a modified miscible flow dispersion-capacitance model. Starting with earlier approaches, we have synthesized an algorithm that provides rapid and accurate determination of the three parameters included in the model: the dispersion coefficient, the flowing fraction of displaceable volume, and the rate constant for mass transfer between flowing and stagnant volumes. Quality of fit is verified with a finite-difference simulation. The dependencies of the three parameters have been evaluated as functions of the displacement velocity and of the water saturation within four carbonate cores composed of various amounts of matrix, vug, and fracture porosity. Numerical simulation of a composite core made by stacking three of the individual cores has been compared with the experimental data. For comparison, an analysis of Berea sandstone gas displacement also has been provided. Although the sandstone displays a minor dependence of gas recovery on water saturation, we found that the carbonate cores are strongly affected by water content. Such behavior would not be measurable if small carbonate samples that can reflect only matrix properties were used. This study therefore represents a significant assessment of the dispersion-capacitance model for carbonate cores and its ability to reflect changes in pore interconnectivity that accompany water saturation alteration. Introduction Miscible displacement processes are used widely in various aspects of oil recovery. A solvent slug injected into a reservoir can be used to displace miscibly either oil or gas. The necessary slug size is determined by the rate at which deterioration can occur as the slug is Another commonly used miscible process involves addition of a small slug within the injected fluids or gases to determine the nature and extent of inter well communication. The quantity of tracer material used is dictated by analytical detection capabilities and by an understanding of the miscible displacement properties of the reservoir. We can develop such understanding by performing one-dimensional (1D) step-change miscible displacement experiments within the laboratory with selected reservoir core material. The effluent profiles derived from the experiments then are fitted to a suitable mathematical model to express the behavior of each rock type through the use of a relatively small number of parameters. This paper illustrates the efficient application of the three-parameter, dispersion-capacitance model. Its application previously has been limited to use with small homogeneous plugs normally composed of intergranular and intencrystalline porosity, and its suitability for use with cores displaying macroscopic heterogeneity has been questioned. Consequently, in addition to illustrating its use with a homogeneous sandstone, we fit data derived from previously reported full-diameter carbonate cores. As noted earlier, these cores were heterogeneous, and each of them displayed different dual or multiple types of porosity characteristic of vugular and fractured carbonate rocks. Dispersion-Capacitance Model The displacement efficiency of one fluid by a second immiscible fluid within a porous medium depends on the complexity of rock and fluid properties. SPEJ P. 647^

Research and application of seismic porosity inversion method for carbonate reservoir based on Gassmann’s equation

2021 ◽  
Vol 13 (1) ◽  
pp. 122-129
Author(s):  
Kaiyuan Liu ◽  
Li Qin ◽  
Xi Zhang ◽  
Liting Liu ◽  
Furong Wu ◽  
...  
Keyword(s):  
High Accuracy ◽  
Field Application ◽  
Carbonate Reservoir ◽  
Carbonate Reservoirs ◽  
Pore Geometry ◽  
Inversion Method ◽  
Well Testing ◽  
Seismic Attribute ◽  
Gassmann’S Equation ◽  
Inclusion Theory

Abstract Carbonate rocks frequently exhibit less predictable seismic attribute–porosity relationships because of complex and heterogeneous pore geometry. Pore geometry plays an important role in carbonate reservoir interpretation, as it influences acoustic and elastic characters. So in porosity prediction of carbonate reservoirs, pore geometry should be considered as a factor. Thus, based on Gassmann’s equation and Eshelby–Walsh ellipsoidal inclusion theory, we introduced a parameter C to stand by pore geometry and then deduced a porosity calculating expression from compressional expression of Gassmann’s equation. In this article, we present a porosity working flow as well as calculate methods of every parameter needed in the porosity inverting equation. From well testing and field application, it proves that the high-accuracy method is suitable for carbonate reservoirs.

A Robust Methodology To Simulate Water-Alternating-Gas Experiments at Different Scenarios Under Near-Miscible Conditions

SPE Journal ◽  
2017 ◽  
Vol 22 (05) ◽  
pp. 1506-1518 ◽  
Author(s):  
Pedram Mahzari ◽  
Mehran Sohrabi
Keyword(s):  
Porous Media ◽  
History Matching ◽  
Flow In Porous Media ◽  
Phase Flow ◽  
Two Phase ◽  
Matching Process ◽  
Water Alternating Gas ◽  
Three Phase ◽  
Three Phase Flow ◽  
Cycle Dependent

Summary Three-phase flow in porous media during water-alternating-gas (WAG) injections and the associated cycle-dependent hysteresis have been subject of studies experimentally and theoretically. In spite of attempts to develop models and simulation methods for WAG injections and three-phase flow, current lack of a solid approach to handle hysteresis effects in simulating WAG-injection scenarios has resulted in misinterpretations of simulation outcomes in laboratory and field scales. In this work, by use of our improved methodology, the first cycle of the WAG experiments (first waterflood and the subsequent gasflood) was history matched to estimate the two-phase krs (oil/water and gas/oil). For subsequent cycles, pertinent parameters of the WAG hysteresis model are included in the automatic-history-matching process to reproduce all WAG cycles together. The results indicate that history matching the whole WAG experiment would lead to a significantly improved simulation outcome, which highlights the importance of two elements in evaluating WAG experiments: inclusion of the full WAG experiments in history matching and use of a more-representative set of two-phase krs, which was originated from our new methodology to estimate two-phase krs from the first cycle of a WAG experiment. Because WAG-related parameters should be able to model any three-phase flow irrespective of WAG scenarios, in another exercise, the tuned parameters obtained from a WAG experiment (starting with water) were used in a similar coreflood test (WAG starting with gas) to assess predictive capability for simulating three-phase flow in porous media. After identifying shortcomings of existing models, an improved methodology was used to history match multiple coreflood experiments simultaneously to estimate parameters that can reasonably capture processes taking place in WAG at different scenarios—that is, starting with water or gas. The comprehensive simulation study performed here would shed some light on a consolidated methodology to estimate saturation functions that can simulate WAG injections at different scenarios.

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