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KFUPM
Vermiculite Particles as an Additive for Oil Well Cement
Ahmed, Abdulmalek (KFUPM) | Elkatatny, Salaheldin (KFUPM) | Adebayo, Abdulrauf R. (KFUPM) | Mahmoud, Ahmed Abdulhamid (KFUPM)
ABSTRACT The process of cementing a well is essential to ensuring the well doesn't collapse. The cement slurry is pumped into the annulus between the casing and the formation. Cement restricts the movement of formation fluids while also supporting the casing. Different types of additives have been used in the design of cement to alter cement properties to meet downhole requirements. This study aims to investigate the impact of vermiculite particles on heavy weighted oil well cement. Base cement and vermiculite cement were prepared and cured under high pressure and high temperature. Viscosity was used to describe the slurry properties, while for the hardened cement samples, the elastic, petrophysical, and strength properties were described by the Poisson's ratio, permeability, and compressive strength, respectively. Moreover, the particles settling was measured by the density variation across the cement column. It was observed from the results that the performance of vermiculite cement is better than the base cement (without vermiculite). Vermiculite cement exhibits excellent properties compared to the base cement, which represents by lower viscosity (reduction of 8%), higher Poisson's ratio (an increase of 7%), lowere Young's modulus (a reduction of 9.4%), minimizing the density variation by 95%, reducing the cement permeability by 46% and improving the compressive strength by 24%. INTRODUCTION The process of cementing oil wells has been an integral part of the oil industry for many years. Oil well cementing is a process that is used to form a permanent bond between the wellbore and the casing and to seal or isolate formations that contain oil and gas (Ahmed et al., 2022c). It ensures the structural integrity of the well, as well as provides economic benefits. Oil well cementing has been identified as a critical factor in ensuring the structural integrity of oil wells. According to Ingraffe et al. (2014), oil well cementing plays a vital role in preventing the migration of hydrocarbons from one formation to another. This is because the cement forms a barrier between the formation and the wellbore, thus preventing any contaminants from entering the wellbore. Furthermore, the cement can also help to prevent the formation of fractures, which can cause a loss of pressure and weaken the integrity of the wellbore. In addition to this, the cement also helps to protect the wellbore from corrosion and other environmental factors that can cause structural damage.
Abstract While many factors influence the success of a given well, the permeability of the surrounding formation is one of the most important properties to understand the nature of any reservoir and to be utilized for effective oil and gas drilling. Gathering data from well logs for different wells can be highly expensive and time-consuming. The goal of this work is to find the best artificial intelligent model which can predict the permeability values with minimum error while saving time and money. Therefore, accurately estimating is highly beneficial to use such a model for further field and engineering applications. In this project, a trial was accomplished through a Machine Learning (ML) approach using several modules of Artificial Intelligent including ANFIS and ANN to examine and build a permeability prediction model based on nine (9) well-logging parameters taken from well-logging data measured at a borehole in carbonate rock. The permeability was predicted from well-log data using Artificial Intelligent (AI) technique. Field data were recorded at one borehole, where all logs are correlated together. After obtaining results, the prediction model can be considered successful, it is highly recommended to utilize ANFIS- Genfis2 as it gives outstanding results as the correlation coefficient training was 1.0 and testing was 0.9347 compared with ANFIS-Genfis1 which was not satisfying with training correlation coefficient of 1.0 and testing 0.4073, including a significant reduction in the percentage error of 14.3% compared of 301%, and utilize ANN with a double layer not single, as the result of single layer showed a correlation coefficient of 0.9337 in training and 0.9924 in testing. In addition, single layer method showed higher error compared with double layer. Conclusively, it is recommended to apply the model with other data obtained from the same reservoir, to minimize the number of unneeded data, enhance the measurement performance by avoiding human errors, and develop other relationships between a set of parameters that can result in a better and most effective prediction model. In novelty, utilizing and studying the output of this trial application of the machine learning approach will summarize the best models and techniques for predicting many important reservoir properties such as Permeability. The number of well logging parameters is high and has been statically analyzed to increase the resolution of the input data. Building this prediction model will increase the recovered amount from the subsurface and will lead to significant cost savings in drilling and exploration operational
- Asia > Middle East > Saudi Arabia (0.29)
- Europe > Norway > Norwegian Sea (0.24)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Abstract Cement needs to be designed to prevent short-term and long-term gas migration scenarios. In the short term, the cement must prevent gas flow immediately following cement placement. In the long term, the cement must resist pressure and temperature cycling during drilling and production. Wells are subjected to substantial stresses from pressure and well testing, injection and stimulation treatments, thermal effects, production cycling, and changes in the surrounding formation over years. Due to these stresses, the cement sheath damage can occur during drilling, perforation and stimulation, and subsequent production. Polymer latex has been used to help reduce the fluid loss to minimize gas migration and aid in the mixing ability of cement slurries. However, there are some engineers who believe that polymer latex will improve cement mechanical properties. The objective of this study is to evaluate the effect of polymer latex on cement mechanical properties after curing at elevated temperature and pressures. Experimental work in this study includes formulations at different polymer latex concentrations, densities measurements, rheology measurement, fluid loss testing, thickening time tests, curing for 30 days at elevated temperature and pressure, and finally mechanical properties measurements (Young's modulus, Poisson's ratio and compressive strength). The study shows that polymer latex will improve mechanical properties of cementing which will lead to improved wellbore integrity.
- Asia > Middle East > Saudi Arabia (0.69)
- Europe (0.68)
- Energy > Oil & Gas > Upstream (1.00)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (0.74)
- Well Drilling > Wellbore Design > Wellbore integrity (1.00)
- Well Drilling > Drilling Operations (1.00)
- Well Drilling > Drilling Fluids and Materials (1.00)
- (3 more...)
Machine Learning for Prediction of CO2 Minimum Miscibility Pressure
Shakeel, Muzammil (KFUPM) | Khan, Mohammad Rasheed (SLB) | Kalam, Shams (KFUPM) | Khan, Rizwan Ahmed (KFUPM) | Patil, Shirish (KFUPM) | Dar, Usman Anjum (SLB)
Abstract Minimum miscibility pressure (MMP) is defined as the minimum pressure at which the CO2 is dissolved in the oil phase inside the reservoir. Minimum miscibility pressure (MMP) plays a critical role in the CO2 injection process during miscible CO2 flooding. Experimentally, MMP is determined by slim-tube experiments, rising bubble method etc. However, experimental analysis is time consuming and can have high associated cost. Therefore, application of Artificial Intelligence (AI) techniques can assist in predicting the MMP based on the available input data. This will save significant time and efforts and predicted the MMP results faster and convenient way. Some authors have worked with AI tools to predict MMP, but the model proposed in this paper has a relatively lower error. Thus, the proposed model in this study is an improved model for the prediction of MMP for miscible CO2 flooding applications. A detailed optimization was carried out in this study for both ANN and ANFIS predictive tools. Single hidden layer with 12 neurons and โtrainlmโ as training algorithm was found out after ANN optimization, whereas subtractive clustering with cluster radius of 0.3 was the optimum scenario for ANFIS technique. ANN prediction was overall better than ANFIS technique for the prediction of CO2 MMP.
A Characterization of Tight Sandstone: Effect of Clay Mineralogy on Pore-Framework
AlKharraa, Hamad Salman (TU Delft) | Wolf, Karl-Heinz A. A. (TU Delft) | Kwak, Hyung T. (Saudi Aramco) | Deshenenkov, Ivan S. (Saudi Aramco) | AlDuhailan, Mohammed A. (Saudi Aramco) | Mahmoud, Mohamed A. (KFUPM) | Arifi, Suliman A. (KFUPM) | AlQahtani, Naif B. (KACST) | AlQuraishi, Abdulrahman A. (KACST) | Zitha, Pacelli L. J. (TU Delft)
Abstract Macro-, meso-, micro-pore systems combined with clay content are critical for fluid flow behavior in tight sandstone formations. This study investigates the impact of clay mineralogy on pore systems in tight rocks. Three outcrop samples were selected based on their comparative petrophysical parameters (Bandera, Kentucky, and Scioto). Our experiments carried out to study the impact of clay content on micro-pore systems in tight sandstone reservoirs involve the following techniques: Routine core analysis (RCA), to estimate the main petrophysical parameters such as porosity and permeability, X-ray diffraction (XRD), and scanning electron microscopy (SEM) to assess mineralogy and elemental composition, Mercury Injection Capillary Pressure (MICP), Nuclear Magnetic Resonance (NMR), and Micro-Computed Tomography (Micro-CT) to analyze pore size distributions. Clay structure results show the presence of booklets of kaolinite and platelets to filamentous shapes of illite. The Scioto sample exhibits a micro-pore system with an average pore body size of 12.6ยฑ0.6 ฮผm and an average pore throat size of 0.25ยฑ0.19 ฮผm. In Bandera and Kentucky samples illite shows pore-bridging clay filling with an average mineral size of around 0.25ยฑ0.03 ฮผm, which reduces the micro-pore throat system sizes. In addition, pore-filling kaolinite minerals with a diameter of 5.1ยฑ0.21 ฮผm, also reduce the micro-pore body sizes. This study qualifies and quantifies the relationship of clay content with primary petrophysical properties of three tight sandstones. The results help to advance procedures for planning oil recovery and CO2 sequestration in tight sandstone reservoirs.
- Asia > China (1.00)
- Asia > Middle East (0.94)
- North America > United States > Kentucky (0.48)
- Research Report > New Finding (0.68)
- Research Report > Experimental Study (0.49)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (1.00)
- Geology > Mineral > Silicate > Phyllosilicate (1.00)
- Government > Regional Government > North America Government > United States Government (1.00)
- Energy > Oil & Gas > Upstream (1.00)
Abstract In a move towards development of sustainable and efficient hydrocarbon production, the industry looks forward to the deployment of carbon neutral and even carbon negative solutions. Accordingly, CO2 EOR is a viable option to improve recovery and has been applied in mature fields for over four decades. The downsides of poor sweep efficiency linked to viscous fingering and gravity segregation can be sorted through generation of CO2 foams in the reservoir. This work proposes the utilization of machine learning techniques, to predict foam flood performance which will thereby aid in optimization of laboratory core-flood experiments. This work is based upon consumption of large set of existing laboratory data collected from literature, amounting to more than 200 data points. The dataset reports core oil recovery factor as a function of three reservoir parameters including porosity, permeability, initial oil saturation. While injected foam volume and total pore volume are also considered. Furthermore, the data records contain experiments for various foaming agent types which are catered for during the machine learning model development through the implementation of numerical tags. The input data is then divided in training subset for development of XGBoost model, complemented by integration of exhaustive grid search and k-fold cross validation techniques. Subsequently, the testing subset is reserved to measure efficacy of the developed model. The model development process involves tuning of machine learning algorithm hyperparameters which control the resultant accuracy, while at the same time it is ensured that the issue of model overfitting is avoided. Testing of the established model is carried out through an array of statistical measures including the R and RMSE values. The proposed model is compared with actual experimental data. The machine learning model can achieve high accuracy in predictive mode for the output parameters. Through statistical error analysis performance measurement, it is observed that the machine learning model can predict CO2 foam flood performance with high R of around 0.99 and low errors. The excellent accuracy of the XGBoost model is credited to the complex processing involved with intelligent algorithms that can discover underlying relationships among the input variables.
Machine Learning for Prediction of CO2 Minimum Miscibility Pressure
Shakeel, Muzammil (KFUPM) | Khan, Mohammad Rasheed (Schlumberger) | Kalam, Shams (KFUPM) | Khan, Rizwan Ahmed (KFUPM) | Patil, Shirish (KFUPM)
Abstract Minimum miscibility pressure (MMP) is defined as the minimum pressure at which the CO2 is dissolved in the oil phase inside the reservoir. Minimum miscibility pressure (MMP) plays a critical role in the CO2 injection process during miscible CO2 flooding. Experimentally, MMP is determined by slim-tube experiments, rising bubble method etc. However, experimental analysis is time consuming and can have high associated cost. Therefore, application of Artificial Intelligence (AI) techniques can assist in predicting the MMP based on the available input data. This will save significant time and efforts and predicted the MMP results faster and convenient way. Some authors have worked with AI tools to predict MMP, but the model proposed in this paper has a relatively lower error. Thus, the proposed model in this study is an improved model for the prediction of MMP for miscible CO2 flooding applications. A detailed optimization was carried out in this study for both ANN and ANFIS predictive tools. Single hidden layer with 12 neurons and โtrainlmโ as training algorithm was found out after ANN optimization, whereas subtractive clustering with cluster radius of 0.3 was the optimum scenario for ANFIS technique. ANN prediction was overall better than ANFIS technique for the prediction of CO2 MMP.
A Deep Learning Formation Image Log Classification Framework for Fracture Identification โ A Study on Carbon Dioxide Injection Performance for the New Zealand Pohokura Field
Katterbauer, Klemens (Saudi Aramco) | Qasim, Abdulaziz (Saudi Aramco) | Al Shehri, Abdallah (Saudi Aramco) | Al Zaidy, Rabeah (KFUPM)
Abstract We have presented a new deep learning framework for the detection of fractures in formation image logs for enhancing CO2 storage. Fractures may represent high velocity gas flow channels which may make CO2 storage a challenge. The novel deep learning framework incorporates both acoustic and electrical formation image logs for the detection of fractures in wellbores for CO2 storage enhancement and injection optimization. The framework was evaluated on the Pohokura-1 well for the detection of fractures, with the framework exhibiting strong classification accuracy. The framework could accurately classify the fractures based on acoustic and electrical image logs in 98.1 % for the training and 85.6 % for the testing dataset. Furthermore, estimates of the fracture size are strong, indicating the ability of the framework to accurately quantify fracture sizes in order to optimize CO2 injection and storage.
- Geology > Geological Subdiscipline (1.00)
- Geology > Structural Geology > Tectonics (0.69)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock (0.48)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (0.49)
- South America > Brazil > Brazil > South Atlantic Ocean > Santos Basin (0.99)
- Oceania > New Zealand > North Island > Tasman Sea > Taranaki Basin > Mangahewa Formation (0.99)
- Oceania > New Zealand > North Island > Tasman Sea > Taranaki Basin > Block PMP 38154 > Pohokura Field > Mangahewa Formation > Pohokura-1 Well (0.99)
- (4 more...)
- Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- (2 more...)
Estimating Reservoir Pressure Gradient from Stage-By-Stage Pressure Fall Off Analysis in Shale Horizontal Wells
Ibrahim, Mazher (Shear Frac Group LLC) | Ibrahim, Ahmed Farid (KFUPM) | Sinkey, Matt (Shear Frac Group LLC) | Johnston, Thomas (Shear Frac Group LLC) | McMurray, Andrew (Shear Frac Group LLC)
Abstract The pressure fall-off data at the end of pumping a hydraulic fracture stage is typically recorded for 15 to 30 minutes and, in some cases, could be extended to an hour. However, this data is never analyzed and often ignored due to the lack of suitable models for estimating the fracture characteristics. This paper introduces a method to analyze these fall-off data to diagnose each frac stage by calculating stimulated surface area, stage permeability, stage fracture half-length, and stage productivity index (Ibrahim et al 2020). In this study, we present a novel approach to estimate reservoir pressure for each stage from the fall-off data analysis. A new method has been developed to estimate the final frac stage pressure gradient after the frac is complete. This method is based on using pumping and leak-off data to calculate each stage pressure which will be used to match frac stage geometry from fracture half-length, fracture conductivity, fracture face skin, and stage productivity index. The estimated stage pressure gradient will help in estimating the amount of depletion from well to well. A field case for three shale oil wells will be presented and calibrated with the existence of the Diagnostic Fracture Injection Test or DFIT method to confirm the accuracy of this method in estimating each frac stage pressure gradient. This new method can be used to help minimize Fracture Driven Interactions (FDIs) between parent and child wells. This paper proposes a novel, low-cost method for estimating all frac stage pressure gradients without performing actual DFIT tests which could cut cost and time of completion. The findings of this paper help improve the efficiency of multistage hydraulic fracturing stimulation of horizontal wells. Introduction In shale-gas reservoirs, the ultralow-permeability matrix is unable to flow gas at a viable rate and provide an adequate drainage volume. Horizontal drilling with multistage fracture treatment has become the key technology for the development of shale-gas reservoirs (Beckwith 2011; King 2010; Wiley et al. 2004). Wells require a series of fracturing operations, with a high pump rate, large fracturing fluid volume, and low proppant concentration, with multi-stage design in order to produce at economic rates (Seale, Donaldson, and Athans 2006). Different hydraulic fracturing fluid systems that can be used in the fracturing process include cross-linked high viscosity systems, foam-based fluids, and slick water.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
Abstract We have investigated the interfacial properties at a brine-hydrocarbon boundary with the prospect of understanding the crystallization process that takes place when certain electrolytes are present in the brine and when certain surfactants are present in the hydrocarbon phase. This was done in an optical force tensiometer setup with a so-called buoyant droplet configuration. It is only specific combinations (that is not all surfactants not all electrolytes) that form crystals and we aim at utilizing this specificity to form crystal plugs in particular sections of an oil reservoir, for example in zones with high flow that can then be reduced by the crystal plugs. The treatment can potentially be tailored based on the predominant acid-type in a mixture. The current study reveals several (at least three) different modes of crystal formation. The electrolyte-surfactant combination that gives rise to the most clear-cut formation of crystals directly at the interface is involving Zn or Cu and dodecanoic acid (C11H23COOH). Several of the systems under study appears to be forming crystals within the hydrocarbon phase and that these crystals more the likely are a result of the surfactant associated diffusive transfer of cations into the hydrocarbon phase. The next short-term goal is to induce crystals when the hydrocarbon phase is (potentially spiked) crude oil to tailor the discoveries towards the longer-term goal: In-situ deep conformance control field applications.