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Zeng, Yongchao (Rice University) | Kamarul Bahrim, Ridhwan Z. (Petronas) | Groot, J. A. W. M. (Shell Global Solutions International) | Vincent-Bonnieu, Sebastien (Shell Global Solutions International) | Groenenboom, Jeroen (Shell Malaysia) | Mohd Shafian, Siti Rohaida (Petronas) | Abdul Manap, Arif A (Petronas) | Tewari, Raj D. (Petronas) | Mohammadian, Erfan (Universiti Teknologi MARA) | Azdarpour, Amin (Islamic Azad University) | Hamidi, Hossein (University of Aberdeen) | Biswal, Sibani L. (Rice University)
Summary This paper advances the understanding of foam transport in heterogeneous porous media for enhanced oil recovery (EOR). Specifically, we investigate the dependence of methane foam rheology on the rock permeability at the laboratory scale and then extend the observations to the field scale with foam modeling techniques and reservoir simulation tools. The oil recovery efficiency of conventional gasflooding, waterflooding, and water‐alternating‐gas (WAG) processes can be limited by constraints such as bypassing effects (including both viscous fingering and channeling mechanisms) and gravity override. The problem can be more severe if the reservoir is highly fractured or heterogeneously layered in the direction of flow. Foam offers the promise to address the three issues simultaneously by better controlling the mobility of injected fluids. However, limited literature data of foam‐flooding experiments were reported using actual reservoir cores at harsh conditions. In this paper, a series of methane (CH4) foam‐flooding experiments were conducted in three different actual cores from a proprietary reservoir at an elevated temperature. It is found that foam rheology is significantly correlated with the rock permeability. To quantify the mobility control offered by foam, we calculated the apparent viscosity on the basis of the measured pressure drop at steady state. Interestingly, the apparent viscosity was found to be selectively higher in the high‐permeability cores compared with that in the low‐permeability zones. We parameterized our system using a texture‐implicit‐local‐equilibrium model (STARS™ simulator, Computer Modelling Group, Calgary, Alberta, Canada) to illustrate the dependence of foam parameters on rock permeability. In addition, we created a two‐layered model reservoir using an in‐house simulator called modular reservoir simulator (MoReS; Shell Research, Rijswijk, The Netherlands) to elucidate the role of different driving forces for fluid diversion at the field level. We took into consideration the combined effect of gravitational, viscous force, and capillary forces in our simulation. We show that the gravitational forces prevent the gas from sweeping the lower part of the reservoir. However, the poor sweep can be ameliorated by intermittent surfactant injection to generate foam. In addition, the capillary force which hinders the gas (nonwetting phase) from entering the low‐permeability region can be effectively leveraged to redistribute the fluids in the porous media, resulting in better sweep efficiency. We conclude that foam if properly designed can effectively improve the conformance of the WAG EOR in the presence of reservoir heterogeneity.
Discontinuities such as fault planes, joints and bedding planes in a rock mass may be filled with different types of fine-grained material that are either transported or accumulated as gouge due to weathering or joint shearing . Filling materials are of the most important geotechnical parameters of discontinuities that have great effect on the shear strength of the joints. This research tries to find a logical relation between uniaxial compressive strength (UCS) of the filling materials with shear strength of the filled discontinuities. For this purpose, joints are made artificially in laboratory scale and connected to each other with different combination of gypsum & clay mortar as the filling materials in dry condition.
According to the conducted tests and analysis of the relevant diagrams, it is concluded that joints with higher UCS of filling materials have higher shear strength so that they have greater value in cohesion (C) and smaller value in friction angel (Φ).
Generally rock masses present in nature are characterized by discontinuities such as joints, fractures and other planes of weakness. Discontinuities that are infilled with fine-grained material which is either transported or appears as a result of weathering or joint shearing, will adversely affect the behaviour of the rock mass . These fine infill materials may drastically reduce the shear strength of the rock joints compared to an unfilled or clean joint, because they may prevent the walls of the rock joint from coming into contact during shear. Hence, the investigation of shear behavior of the joints is of prime importance .
The shear strength of a filled joint is often assumed to be that of the infill material alone, if the infill thickness is higher than a certain critical value. for smaller values of infill thickness, the rock-to-rock contact influence becomes increasingly prominent.
In this study, a series of laboratory test carried out on artificial and idealized models of rock joints in order to determine the relation between the uniaxial compressive strength of the filling material with cohesion and frictional properties of joints using Mohr-Coulomb criteria. The tests carried out in dry condition and joints have no roughness (smooth joints).
ABSTRACT: Rate of Penetration (ROP) estimation is a key parameter in drilling optimization, due to its role in minimizing drilling costs. Several ROP models have been developed which can predict the penetration rate based on physics-based or data-driven techniques. Considering a data-driven approach, the purpose of this research is to apply a Machine Learning (ML) algorithm named Ensemble Bagged Trees to predict the rate of penetration (ROP) in formations based on data of weight on bit (WOB), rotary speed (RPM), torque and measured depth. In this study, a large well segment in Iran has been analyzed in which there is no information break throughout the segment. Based on the achieved high accuracy, it is concluded that proposed machine learning algorithm is a very useful and good predictor of rate of penetration through wellbore. The parameters to evaluate the accuracy of the model were mean squared error and correlation coefficient on the testing data.
ABSTRACT: Wellbore strengthening is an extensively-used method to reduce lost circulation in the petroleum drilling industry, with adding Lost Circulation material to the drilling mud and bridging the fractures on the wellbore to increase maximum stable pressure. In this study, the finite element and Kirsch analytical methods used to model the hoop stress distribution and its effective factors, in one of South Pars gas field's formations, based on Persian Gulf. Findings showed that the compressive stress, in the single fracture model, is raised up to the area of 30° in the fracture initiation state and it will be more in the bridging location across the fracture faces. Furthermore, the hoop stress at the tip of the fracture tends to be tensile; moreover, the compressive stress with higher wellbore pressure on the wellbore, before the area of 60° and after bridging the fracture, is greater than the compressive stress with lower wellbore pressure on the wellbore wall and it will be reversed after the area of 60°. In the multi-fracture model, by moving away from the first fracture, the compressive stress decreases around the 90°, due to the existence of second fracture and the compression stress is raised by increasing the horizontal stress contrast.
Wellbore strengthening is a practical method for reducing lost circulation while drilling formations with narrow drilling mud weight windows. It increases the wellbore's maximum sustainable pressure by bridging drilling induced or natural fractures with lost circulation material (Feng and Gray, 2016). To keep downhole pressure within the mud-weight window, drilling fluids and lost circulation material (LCM) are considered to make wellbore-hydrodynamic pressure low enough to evade downhole lost circulation but high sufficient to avoid borehole instability or kicking(Feng et al., 2015). These drilling fluids and additives cause in the formation hoop stress enhancement, called stress cage, which is a near wellbore area of high stress induced by propping open and sealing narrow fractures at the wellbore/formation boundary (Alberty and McLean, 2004). All lost circulation materials are not same and their type plays a role in terms of both plugging and toughness to better endure displacement pressures. It also, has been confirmed that, mostly, combinations of LCMs act more efficiently compared with the practice of only one type in wellbore strengthening (Savari et al., 2014). Some companies are produced a designer mud which effectively increases fracture resistance while drilling, which can be valuable in both shale and sandstone.it acts by forming a stress cage, using particle bridging and some type of fluid loss mud (Aston et al., 2004). In recent years many deep fundamental studies has been done, related to the lost circulation and wellbore strengthening (Feng and Gray, 2017; Feng et al., 2016). To better understanding of basics of the process of Wellbore strengthening, the effects of several parameters are still not fully understood, and a complete parametric study for each type of formations is necessary to improving field operations. There are plenty of numerical models and analytical solutions which have been developed in recent years for that reason. (AlBahrani and Noynaert, 2016; Wang et al., 2007; Mehrabian et al., 2015; Zhong et al., 2017;Salehi and Nygaard, 2014; Kiran and Salehi,2016; Salehi and Nygaard, 2011; Shahri et al., 2015; Zhang et al., 2016; Zhang et al., 2017;Wang et al., 2018; Chellappah et al., 2018; Feng et al., 2018; Wang, 2018); besides, some research has been done for the usage of wellbore strengthening methods for depleted reservoirs.(Shahri et al., 2014). Furthermore, a set of analytical equations, considered their advantages and disadvantages, are developed for parametric analysis of typical wellbore strengthening approaches. (Morita and Fuh, 2011). A finite-element method is the most important numerical technique, used today to model the wellbore strengthening problems, has been developed to research the effects of major parameters on the distribution of near wellbore hoop stress and fracture width (Feng and Gray, 2016; Arlanoglu et al., 2004; Towler, 2007). In this research, the term hoop stress is generally used to mean the circumferential stress at the wellbore wall. The hoop or tangential stress around a wellbore wall is the main factor in borehole stability and integrity analysis. This research investigates different and effective parameters of wellbore strengthening, related to the formations of south pars gas field in Persian gulf and numerical and analytical methods are used for this purpose; besides, new numerical model with multi fractures has been created to better understanding of wellbore strengthening mechanism and related effective parameters, to investigate of hoop stress around the wellbore and the width of the fractures.
ABSTRACT: In this study, an artificial neural networks (ANN) model as an artificial intelligence (AI) technique is proposed to determine the formation pore pressure from data of two critical drilling parameters named mechanical specific energy and drilling efficiency. These parameters (MSE and DE) which are closely correlated to differential pressure during drilling were chosen as a result of a literature review of proposed methods of pore pressure estimation. Collected data of a three wellbores drilled in an Iranian sandstone formation were used for the purpose of this research, and pore pressure estimated using this model was in a good agreement with estimates from previously published models including the one derived from conventional sonic logs data. The proposed model results were analyzed, and proved that artificial neural networks are capable to provide reliable independent predictions of pore pressure, and this smart model can be hired to analyze data for pre-drilling prediction models construction and post-well prediction models optimization.
Nowadays, many drilling operations are not being performed with optimum efficiency and management of costs, time and quality. Thus, drilling optimization has become an important and critical challenge for drilling operators in the petroleum industry as there are a lot of variables for consideration in drilling systems optimization. Real-time analysis of drilling parameters’ data is a way to understand drilling mechanics and efficiency. (Amadi and Iyalla, 2012)
Estimates of Formation Pore Pressure before and while drilling, and recognizing deviations from the expected pressure are important inputs for well planning and operational decision making. The effect of Differential Pressure (wellbore pressure minus pore pressure) on drilling responses has long been recognized, and drilling efficiency (DE) and mechanical specific energy (MSE) are chosen as parameters highly correlated to the differential pressure. (Majidi et al. 2016)
Logs and drilling mechanics based estimation methods are independent models of pore pressure estimation when suitable data are available. However, the advantage of drilling mechanics method is that it can provide pressure while drilling in real-time at the bit, not behind the bit, and the error would be diminished.
ABSTRACT: Asmari and Sarvak limestones are two main oil producer formations in Iran and the Middle East. The production and optimal utilization of these reservoirs will have a significant impact on the economy of the petroleum industry. Geomechanical modelling of oil reservoirs are widely used in optimum drilling, production and reservoir compaction. Hence, the static Young’s modulus (Es) is one of the most essential parameters for any reservoir geomechanical modelling. However, information on the values of Es along the well depth is often discontinuous and limited to the core locations. Therefore, dynamic Young’s modulus (Ed) determined from open-hole log data such as density and compressional and shear wave velocities could result in continuous estimation of elastic properties of the formations versus depth. Nevertheless, static parameters are more reliable than the dynamic parameters and they are widely accepted by geomechanics around the world. The relationship between the static and dynamic elastic modulus in rock materials has been frequently addressed in scientific literature. Overall, when it comes to the study of materials with a wide range of elastic moduli, the functions that best represent this relationship are non-linear and do not depend on a single parameter. Therefore, finding a valid correlation between static and dynamic parameters could result in a continuous and more reliable knowledge on elastic parameters. In this study, published data of the tests which were carried out on 45 Asmari and Sarvak limestone core specimens are used. Then, as an artificial intelligence method, artificial neural networks were developed to correlate Es and Ed data. After comparing the results of the suggested method with correlations which were established between dynamic and static measurements, a good agreement was observed. The accuracy of the obtained results have shown that artificial neural networks are appropriate tools to predict the values of Es based on Ed data of limestone formations.
Development of an Excavation Damaged Zone around an underground excavation can change the physical, mechanical and hydraulic behaviours of the rock mass near the underground space. This paper presents an approach to build a prediction model for the assessment of EDZ based on an artificial intelligence method called artificial neural networks which are applied to build a prediction model for the assessment of EDZ using data of geological and blasting parameters which are chosen as a result of a literature review. Upon developing the model to evaluate rock damage from underground blasts, practical applications were accomplished for confirmation. Results showed that, because of their high accuracy in establishment of a correlation between EDZ and input parameters' data, ANNs are appropriate tools to predict excavation damaged zone using data of parameters including perimeter powder factor, rock mass quality, tensile strength, density, wave velocity, vibration propagation coefficients and explosive detonated per delay. 1 INTRODUCTION The extent of excavation damaged zone depends on geological structure, excavation method, overburden, and numerous other parameters. Prediction of this damage is an important factor to evaluate the quality of excavation process in tunnelling and underground mining. It would allow the optimization of explosive charges utilized in successive blasting rounds, as well as lowering risks of instability from rock loosening, less support costs and water inflows. The detonation of explosives confined in boreholes generates a large volume of gases at high pressures and temperatures. The sudden application of these effects to the cylindrical surface of the hole generates a compressive stress pulse in the rock, which may be a source of damage in the surrounding zone. The dimensions of that zone depend on the size of explosive charge detonated, rock's dynamic strength and density, wave velocity propagation, and vibration velocities transmitted to the rock mass. The detonation of explosives confined in boreholes generates a large volume of gases at high temperatures (2000–5000°C) and high pressures (10–40 GPa). The sudden application of these effects to the cylindrical surface of the hole generates a compressive stress pulse in the rock, which may be a source of damage in the surrounding zone. These deviations are normally undesirable because they generate higher costs in the constructive process of the underground opening [Dinis Da Gama, et al., 2002].
Saberhosseini, Seyed Erfan (Islamic Azad University) | Mohammadrezaei, Hossein (Islamic Azad University) | Saeidi, Omid (Iranian Offshore Oil Company) | Zadeh, Nadia Shafie (Natural Resources Canada) | Senobar, Ali (Iranian Offshore Oil Company)
Summary Pre-analysis of the geometry of a hydraulically induced fracture, including fracture width, length, and height, plays a crucial role in a successful hydraulic-fracturing (HF) operation. Besides the geometry of the fracture, the injection rate should be optimal for obtaining desired results such as maintaining sufficient aperture for proppant placement, avoiding screenouts or proppant bridging, and also preventing caprock-integrity failure as a result of an extensively uncontrolled fracture in reservoirs. A sophisticated numerical model derived from the cohesive-elements method has been developed and validated using field data to obtain an insight on the optimal fracture geometry and injection rate that can lead to a safe and efficient operation. The HF operation has been conducted in an oil field in the Persian Gulf with the aim of enhanced oil recovery (EOR) from a limestone reservoir with low matrix permeability in a horizontal wellbore. The concept of the cohesive-elements method with pore pressure as an additional degree of freedom has been applied to a 3D fully coupled HF model to estimate fracture geometry, specifically fracture height as a function of the optimal injection rate in a reservoir porous medium. It was observed that by increasing injection rate, all the fracture-geometry parameters steeply increased, but the fracture height must be controlled to be in the reservoir domain and not surpass the caprock and sublayer. For the reservoir under study with the maximum height of 100 m, length of 250 m, width of 100 m, permeability of 2 md, and porosity of 10%, the optimal fracture height is 73.4 m; the average fracture width and half-length are 12.8 mm and 55.4 m, respectively. Therefore, the optimal injection rate derived from the fracture height and geometry is in this case 4.5 bbl/min. The computed fracture pressure (49.55 MPa = 7,283.85 psi) has been compared with the field fracture pressure (51.02 MPa = 7,500 psi), and the error obtained for these two values is 2.88%, which showed a very good agreement.
One of the necessities in drilling operations is the ability to predict the performance of rock drills. To explain the effects of various parameters on the drilling rate (drilling velocity) and the drilling tool wear, the term drillability is being used. In this research, drillability is defined as a penetration rate. The correlation between drilling rate index (DRI) and some rock properties is inspected in this survey in order to examine the influences of properties of strength indexes and brittleness of rocks on drillability. To achieve this, uniaxial compressive strength (UCS) and Brazilian tensile strength (BTS) values of different rock samples were used as geomechanical properties data. Then, the brittleness of rocks which use the uniaxial compressive strength and tensile strength of rocks were determined from calculations. Afterwards, artificial neural networks (ANN) as an artificial intelligence technique was employed in order to relate datasets of UCS, BTS and brittleness as input data to the DRI as the target. The suggested correlation between DRI and both mechanical rock properties and brittleness concepts were analyzed, and acceptable correlations between drillability of rocks and the input parameters was achieved. It is concluded that by the use of data of uniaxial compressive strength, Brazilian tensile strength and rock brittleness, ANNs can evaluate drilling rate index accurately.
Nowadays, Tunnel excavation utilizing mechanical excavation techniques such as tunnel boring machines (TBM’s) and roadheaders is growingly becoming common. Choosing the machinery and hardware must be under consideration of physical, mechanical and petrographic properties of rock, otherwise it can result in considerable detriments. Hence, earlier than tunnelling operations, it is vital to investigate rock properties (Yarali and Soyer, 2011).
Undoubtedly, Roadheaders are one of the most versatile excavation machine types operated in soft and medium strength rock formations’ tunneling and mining. An essential aspect of a successful roadheader application is definitely the performance prediction which is basically concerned with machine selection, production rate and also bit consumption. Evolving a new roadheaders’ performance prediction model in various operational conditions and also different material is the primary intention of this research. Investigation on previous works revealed that three main features have great influences on the bit wear of a roadheader. Brittleness which can be utilized as a cuttability factor in mechanical excavation perspective is actually one of some parameters which is absolutely in relation with breakage properties. In addition to the rock brittleness, rock quality designation (RQD) and instantaneous cutting rate are employed as input parameters for the prediction of pick (bit) consumption rate (PCR). For the purpose of this paper, using previously published field datasets, a new prediction model using the application of artificial neural networks as an artificial intelligence technique is developed, trained and tested to estimate PCR based on data of brittleness, RQD and instantaneous cutter rate. Results demonstrated that PCR is highly correlated to the input parameters, and the ANN model could produce acceptable predictions.
In recent years, mining business has been under the influences of global trends, environmental limitations, and variant market requirements to be more and more productive and profitable. Utilizing mechanical miners like roadheaders, continuous miners, impact hammers and tunnel boring machines for ore extraction and excavation of development drivages, increases profitability. The mentioned miners result in continuous operations and consequently, the mechanization of mines with mechanical miners is presumed to make mining projects more productive, more competitive, and less costly. As a result, ordinary drill and blast technique could be avoided. Roadheaders which are applicable in tunnelling, mine development, and mine production of rock types of soft to medium strength, are very adaptable excavation facilities. The efficiency of roadheader application is rudimentary related to machine selection, production rate and bit consumption (Ebrahimabadi et al., 2011).