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Elsayed, Mahmoud (King Fahd University of Petroleum and Minerals) | El-Husseiny, Ammar (King Fahd University of Petroleum and Minerals (Corresponding author) | Kwak, Hyung (email: email@example.com)) | Hussaini, Syed Rizwanullah (Saudi Aramco) | Mahmoud, Mohamed (King Fahd University of Petroleum and Minerals)
Summary In-situ evaluation of fracture tortuosity (i.e., pore geometry complexity and roughness) and preferential orientation is crucial for fluid flow simulation and production forecast in subsurface water and hydrocarbon reservoirs. This is particularly significant for naturally fractured reservoirs or postacid fracturing because of the strong permeability anisotropy. However, such downhole in-situ characterization remains a challenge. This study presents a new method for evaluating fracture tortuosity and preferential orientation based on the pulsed field gradient (PFG) nuclear magnetic resonance (NMR) technique. Such an approach provides diffusion tortuosity, τd, defined as the ratio of bulk fluid diffusion coefficient to the restricted diffusion coefficient in the porous media. In the PFG NMR technique, the magnetic field gradient can be applied in different directions, and therefore anisotropy in diffusion coefficient and τd can be evaluated. Three 3D printed samples, characterized by well controlled variable fracture tortuosity, one fractured sandstone, and three acidized carbonate samples with wormhole were used in this study. PFG NMR measurements were performed using both 2- and 12-MHz NMR instruments to obtain τd in the three different principal directions. The results obtained from the NMR measurements were compared with fracture tortuosity and preferential orientation determined from the microcomputed tomography (micro-CT) images of the samples. The results showed that τd increases as the fracture tortuosity and pore geometry complexity increases, showing good agreement with the image-based geometric tortuosity values. Moreover, the lowest τd values were found to coincide with the preferential direction of fracture surfaces and wormhole body for a given sample, whereas the maximum τd values correspond to the nonconnected pathway directions. These results suggest that the implantation of directional restricted diffusion measurements on the NMR well logging tools would offer a possibility of probing tortuosity and determining preferential fluid flow direction via direct downhole measurements.
Summary Various unified gas flow (UGF) and apparent permeability models have been proposed to characterize the complex gas transport mechanisms in shale formations. However, such models are typically expressed as combinations of multiple gas flow mechanisms so that they cannot predict gas velocity profile. In this study, we develop a novel approach to predict the gas velocity profile in the entire Knudsen number (Kn) regime for circular and noncircular (i.e., square, rectangular, triangular and elliptical) nanochannels and investigate the effects of cross-sectional geometry on gas transport in nanochannels. To this end, a new UGF model is proposed to describe the gas flow behaviors in the entire Kn regime, considering the effects of gas slippage, bulk diffusion, Knudsen diffusion, surface diffusion, and cross-sectional geometry of flow channel. In addition, the boundary condition of the semianalytical second-order slip model applicable to various cross-sectional geometries is modified by adjusting the slip coefficients through the comparison between the proposed UGF model and the Navier-Stokes (N-S) equation with second-order slip boundary condition. As a result, the velocity profile of free gas in the entire Kn regime for the nanochannel with a specific cross section can be determined by solving the second-order slip model with adjusted slip coefficients via the finite element method. The results indicate that the geometry of the cross section has a significant influence on the mass flow rate and gas velocity profile in nanochannels. The predicted mass flow rates for the nanochannels with identical hydraulic diameter decrease with the cross-sectional geometry in the sequence as ellipse > equilateral triangle > rectangle > square > circle. However, the ranking of velocity profiles for such nanochannels, which is governed by the cross-sectional geometry, also varies with Kn. These findings indicate that the developed approach can predict the synergetic gas transport (i.e., gas slippage, bulk diffusion, Knudsen diffusion, and surface diffusion) and gas velocity profile in nanochannels with different cross-sectional geometries for a wide range of Kn, which gives insight into the characterization of gas flow behaviors in nanoporous shale.
Guan, Huili (Texas A&M University (now with Lamprogen Inc.)) | Lim, Austin (Texas A&M University (Corresponding author) | Hernandez, Joshua (email: firstname.lastname@example.org)) | Liang, Jenn-Tai (Texas A&M University)
Summary Scale can cause flow assurance issues because of damage to the near-wellbore region and in production facilities. Scale inhibitors are often used to help mitigate these problems. The main focus of this proof-of-concept study is to examine the ability of a newly developed crosslinked nanosized scale inhibitor (NSI) particle to inhibit scale formation through sustained release of scale inhibitor into a model brine and increase scale inhibitor treatment lifetime. Results from minimum inhibition concentration (MIC) measurements showed that, at 95°C, the MIC decreased gradually from 10 ppm at day 0 to 5 ppm after 9 days and eventually reached a very low MIC of 2 ppm after 49 days. These findings are consistent with our hypothesis that the sustained release of linear scale inhibitor from the NSI would result in a decrease in MIC over time caused by an increased amount of linear scale inhibitor being released into the model brine. Also, attaching 2-acrylamido-2-methyl-1-propanesulfonic functional group (AMPS) to NSI successfully inhibits the pseudoscale formation when the scale inhibitor comes into contact with the calcium and magnesium in the model brine. Results from sandpack floods showed that NSI increased the treatment lifetime from 3 pore volumes (PV) postflush throughput, for the traditional scale inhibitor, to 35 to 105 PV postflush throughput. These results support our hypothesis that sustained release of the trapped NSI nanoparticles can improve the treatment lifetime.
Xu, Guoqing (Sinopec Research Institute of Petroleum Engineering (Corresponding author) | Han, Yujiao (email: email@example.com)) | Jiang, Yun (Sinopec Research Institute of Petroleum Engineering) | Shi, Yang (Research Institute of Petroleum Exploration & Development, PetroChina (Corresponding author) | Wang, Mingxian (email: firstname.lastname@example.org)) | Zeng, XingHang (Research Institute of Petroleum Exploration & Development, PetroChina (Corresponding author)
Summary Spontaneous imbibition (SI) is regarded as an effective method to improve the oil recovery in a tight sandstone reservoir, which leads to a significant change in fracturing design and flowback treatment. However, a longtime shut-in period would aggravate the retention of fracturing fluid, which is in contradiction with high production in the field. It is imperative to understand how SI works during shut-in time, so as to maximize the effect of imbibition in oil recovery enhancement. In this study, a series of experiments were conducted to simulate the status of residual oil saturation so that the inner mechanism of imbibition on oil recovery can be investigated. Low-field nuclear magnetic resonance (LF-NMR) was used to provide direct observation of phase changes in different pore sizes. The experimental results show a positive effect of imbibition on residual oil reduction. This phenomenon further elucidates the observations made during the well shut-in, soaking period, and low flowback efficiency. This study aims to understand the mechanism of SI behavior and help to improve the accuracy of production prediction.
Summary A physics-based data-driven model is proposed for forecasting of subsurface energy production. The model fully relies on production data and does not require any in-depth knowledge of reservoir geology or governing physics. In the proposed approach, we use the Delft Advanced Reservoir Terra Simulator (DARTS) as a workhorse for data-driven simulation. DARTS uses an operator-based linearization technique that exploits an abstract interpretation of physics benefiting computational performance. The physics-based datadriven model is trained to fit data increasing the fidelity of the model forecast and reflecting significant changes in reservoir dynamics or physics over its history. The model is examined and validated for both synthetic and real field production data. We demonstrate that the developed approach is capable of providing accurate and reliable production forecast on a daily basis, even if the exact geological information is not available. Introduction Computer technologies are progressing rapidly. Computational capacities that are currently available provide an opportunity for many subsurface applications to perform complex numerical simulations of high-resolution 3D geocellular computer models. Predictions obtained from such models are an important factor governing efficient reservoir management and decision making. The models describe complex geological features through a set of gridblocks and associated rock and fluid properties. However, in many cases, the reliability of geological information is questionable or even not available. Although it is possible to develop a high-fidelity model on a reliable basis of reservoir geology, a high-resolution computer model can exceed a few million blocks and can take hours or even days to simulate. It is still not computationally feasible to perform history matching or reservoir-development optimization at such resolution because it involves a large number of simulation runs. Different methods have been developed to overcome the issue.
AlBahrani, Hussain (Texas A&M University (Corresponding author) | Papamichos, Euripides (email: email@example.com)) | Morita, Nobuo (Aristotle University of Thessaloniki Greece and SINTEF Petroleum Research)
Summary The petroleum industry has long relied on predrilling geomechanics models to generate static representations of the allowable mud weight limits. These models rely on simplifying assumptions such as linear elasticity, a uniform wellbore shape, and generalized failure criteria to predict failure and determine a safe mud weight. These assumptions lead to inaccurate results, and they fail to reflect the effect of different routing drilling events. Thus, this paper’s main objective is to improve the process for predicting the wellbore rock failure while drilling. This work overcomes the limitations by using a new and integrated modeling scheme. Wellbore failure prediction is improved through the use of an integrated modeling scheme that involves an elasto-plastic finite element method (FEM) model, machine learning (ML) algorithms, and real-time drilling data, such as image logs from a logging while drilling (LWD) tool that accurately describes the current shape of the wellbore. Available offset well data are modeled in the FEM code and are then used to train the ML algorithms. The produced integrated model of FEM and ML is used to predict failure limits for new wells. This improved failure prediction can be updated with the occurrence of different drilling events such as induced fractures and wellbore enlargements. The values are captured from real-time data and reflected in the integrated model to produce a dynamic representation of the drilling window. The integrated modeling scheme was first applied to laboratory experimental results to provide a proof of concept and validation. This application showed improvement in rock-failure prediction when compared with conventional failure criteria such as Mohr-Coulomb. Also, offset-well data from wireline logging and drilling records are used to train and build a field-based integrated model, which is then used to show that the model output for a separate test well reasonably matches the drilling events from the test well. Application of this integrated model highlights how the allowable mud-weight limits can vary because drilling progresses in a manner that cannot be captured by the conventional predrilling models. As illustrated by a field case, the improvement in failure prediction through this modeling scheme can help avoid nonproductive time events such as wellbore enlargements, hole cleaning issues, pack-offs,stuck-pipe, and lost circulation. This efficiency is to be achieved by a real-time implementation of the model where it responds to drilling events as they occur. Also, this model enables engineers to take advantage of available data that are not routinely used by drilling.
Summary In this work, we investigate the efficient estimation of the optimal design variables that maximize net present value (NPV) for the life-cycle production optimization during a single-well carbon dioxide (CO2) huff-n-puff (HnP) process in unconventional oil reservoirs. A synthetic unconventional reservoir model based on Bakken Formation oil composition is used. The model accounts for the natural fracture and geomechanical effects. Both the deterministic (based on a single reservoir model) and robust (based on an ensemble of reservoir models) production optimization strategies are considered. The injection rate of CO2, the production bottomhole pressure (BHP), the duration of injection and the production periods in each cycle of the HnP process, and the cycle lengths for a predetermined life-cycle time can be included in the set of optimum design (or well control) variables. During optimization, the NPV is calculated by a machine learning (ML) proxy model trained to accurately approximate the NPV that would be calculated from a reservoir simulator run. Similar to the ML algorithms, we use both least-squares (LS) support vector regression (SVR) and Gaussian process regression (GPR). Given a set of forward simulation runs with a commercial compositional simulator that simulates the miscible CO2 HnP process, a proxy is built based on the ML method chosen. Having the proxy model, we use it in an iterative-sampling-refinement optimization algorithm directly to optimize the design variables. As an optimization tool, the sequential quadratic programming (SQP) method is used inside this iterative-sampling-refinement optimization algorithm. Computational efficiencies of the ML proxy-based optimization methods are compared with those of the conventional stochastic simplex approximate gradient (StoSAG)-based methods. Our results show that the LS-SVR- and GPR-based proxy models are accurate and useful in approximating NPV in the optimization of the CO2 HnP process. The results also indicate that both the GPR and LS-SVR methods exhibit very similar convergence rates, but GPR requires 10 times more computational time than LS-SVR. However, GPR provides flexibility over LS-SVR to access uncertainty in our NPV predictions because it considers the covariance information of the GPR model. Both ML-based methods prove to be quite efficient in production optimization, saving significant computational times (at least 4 times more efficient) over a stochastic gradient computed from a high-fidelity compositional simulator directly in a gradient ascent algorithm. To our knowledge, this is the first study presenting a comprehensive review and comparison of two different ML-proxy-based optimization methods with traditional StoSAG-based optimization methods for the production optimization problem of a miscible CO2HnP.
Gao, Jiaxi (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, and Exploration and Development Technology Research Institute, Yanchang Oil Field Company Limited) | Yao, Yuedong (State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing (Corresponding author) | Wang, Dawen (email: firstname.lastname@example.org)) | Tong, Hang (Exploration and Development Technology Research Institute, Yanchang Oil Field Company Limited)
Summary Supercritical water has been proved effective in heavy-oil recovery. However, understanding the flow characteristics of supercritical water in the wellbore is still in the early stages. In this paper, using the theory of heat transfer and fluid mechanics and combining that with the physical properties of supercritical water, a heat-transfer model for vertical wellbore injection with supercritical water is established. The influence of heat transfer and the Joule-Thomson effect on the temperature of supercritical water are considered. Results show the following: - The predicted values of pressure and temperature are in good agreement with the test values. However, the equivalent pressure of supercritical water at the upper end of the wellbore is higher than the equivalent pressure at the lower end. Introduction The success of the application of steam-stimulation technology is mainly determined by the steam-injection stage (Wu et al. 2018). Inefficient heat carrier and serious steam channeling will reduce the development effect of steam stimulation to varying degrees (De Souza et al. 2018; Xia et al. 2018). In fact, the two problems of low heat-carrier efficiency and serious steam channeling are the core problems faced by all thermal oil-development methods that use thermal fluid injection. Therefore, research on the steam-injection process is mainly performed from the following two aspects: - Research and development of new heat carriers suitable for different development conditions (Huang et al. 2018a) to improve the physical heating effect of the reservoir and further promote the occurrence of chemical reactions such as hydrothermal cracking. With the complexity of the chemical composition and physicochemical properties of the heat carrier and the structure of the steaminjection wellbore, the establishment of a theoretical model with an engineering-application background has important practical significance for optimizing steam-injection parameters and analyzing the heat-transfer law of the wellbore (Dong et al. 2014). In the early stage of wellbore heat-transfer research, researchers usually only studied the wellbore heat-transfer law by interpreting the temperature-logging data at a specific time of fluid injection and shut-in (Nowak 1953). Moss and White (1959) and Lesem et al. (1957) first studied the wellbore heat-transfer dynamics under long-term injection conditions, but they did not establish relevant wellbore heat-transfer models.
Summary Reservoir depletion is known to reduce the porosity and permeability of stress-sensitive reservoir rocks. The effect may substantially hinder the productivity index (PI) of producing wells. This study presents analytical solutions for the time-dependent and steady-state well PIs, respectively, of a bounded, disk-shaped, elastic reservoir with no-flow and constant-pressure conditions at the outer boundary. A combination of Green's functions, the Laplace transform method, and the perturbation technique is used to solve the governing nonlinear partial differential equations of the considered coupled problems of flow and geomechanics. Dimensional analyses based on the Buckingham theorem are conducted to identify the dimensionless parameters groups of each problem and to express the resulting analytical solutions in the dimensionless form. In addition, necessary corrections to an existing error in the reported Green's functions for the induced strain field of a ring-shaped pressure source within an elastic half-space (Segall 1992) are made. The corrected Green's functions are used to obtain the strain induced by the pore fluid pressure distribution within a depleting disked-shaped reservoir. Consequently, a corrected permeability variation model compared to our previously published, time-independent solution for rate-dependent PI (Zhang and Mehrabian 2021a) is presented. Finally, a mechanistically rigorous formulation of the permeability modulus parameter that commonly appears in the pertinent literature is suggested. In addition to the in-house developed finite-difference solutions, the presented analytical solutions are verified against results from the finite-element simulation of the same problems using COMSOL® Multiphysics (2018). The obtained rate-dependent PI of the reservoir is controlled by four dimensionless parameters, namely, the dimensionless rock bulk modulus, the Biot-Willis effective stress coefficient, Poisson's ratio, and rock initial porosity. The pore fluid pressure solution is shown to asymptotically approach the corresponding flow-only solution for large values of the dimensionless rock bulk modulus. Parametric analysis of the solution suggests that the well productivity loss has a reverse relationship with the dimensionless bulk modulus and initial porosity of the rock, whereas a direct relationship is identified with Biot-Willis effective stress coefficient and Poisson's ratio. Compared to the reservoir with a constant-pressure outer boundary, the PI of a reservoir with a no-flow condition at the outer boundary is shown to be more significantly hindered by the stress sensitivity of the reservoir rock.
Johnson, Caroline (Heriot-Watt University–Edinburgh (*Corresponding author) | Sefat, Morteza Haghighat (email: email@example.com)) | Elsheikh, Ahmed H. (Heriot-Watt University–Edinburgh) | Davies, David (Heriot-Watt University–Edinburgh)
Summary In the next decades, tens of thousands of well plugging and abandonment (P&A) operations are expected to be executed worldwide. Decommissioning activities in the North Sea alone are forecasted to require 2,624 wells to be plugged and abandoned during the 10-year period starting from 2019 (Oil&Gas_UK 2019). This increase in decommissioning activity level and the associated high costs of permanent P&A operations require new, fit-for-purpose, P&A design tools and operational technologies to ensure safe and cost-effective decommissioning of hydrocarbon production wells. This paper introduces a novel modeling framework to support risk-based evaluation of well P&A designs using a fluid-flow simulation methodology combined with probabilistic estimation techniques. The developed well-centric modeling framework covers the full range of North Sea P&A well designs and allows for quantification of the long-term (thousands of years) evolution of hydrocarbon movement in the plugged and abandoned well. The framework is complemented by an in-house visualization tool for identification of the dominant hydrocarbon flow-paths. Monte Carlo methods are used to account for uncertainties in the modeling inputs, allowing for robust comparison of various P&A design options, which can be ranked on the basis of hydrocarbon leakage risks. The proposed framework is able to model transient conditions within the well P&A system, allowing for the development of new key performance indicators (e.g., time until hydrocarbons reach surface and changes in hydrocarbon saturation within the P&A well). Such key performance indicators are not commonly used, because most published work in this area relies on steady-state P&A models. The developed framework was used in the assessment of several P&A design cases. The results obtained, which are presented in this paper, demonstrate its value for supporting risk-baseddecision-making by allowing for quantitative comparison of the expected performance of multiple P&A design options for given well/reservoir conditions. The framework can be used for identifying cost-effective, fit-for-purpose P&A designs, for example by optimizing the number, location, and length of wellbore barriers and evaluating the effectiveness of annular cement sheath remedial operations. Additionally, this framework can be used as a sensitivity analysis tool to identify the critical parameters that have the greatest impact on the modeled leakage risks, to guide data acquisition plans and model refinement steps aimed at reducing the uncertainties in key model parameters.