Search

Estimating porosity models via seismic data is challenging due to the low signal-to-noise ratio and insufficient resolution of the data. Although impedance inversion is often used in combination with well logs, to obtain subseismic scale porosity data, several problems must be addressed. Alternatively, we have proposed a machine learning-based workflow to convert seismic data into porosity models. A residual U-Net++ (ResUNet++)-based workflow is designed to take multiple poststack seismic volumes with different frequency bands as input and estimate a corresponding porosity model as output. This workflow is demonstrated in a 3D channelized reservoir to estimate the porosity model, and the R2 score of more than 0.9 is achieved for training and validation data. Moreover, a stress test is performed by adding noise to the seismic data to verify the expandability of applications, and the results find a robust estimation with 5% added noise. The additional two ResUNet++ are trained to only take the lowest or highest resolution seismic data as input to estimate the porosity model, but they exhibit underfitting and overfitting, respectively, supporting the importance of using multiscale seismic data for the porosity estimation problem. We mainly use experimental cases with simulated data. Therefore, scaling ResUNet++ for real data is needed in future research, such as considering coherent noise in seismic data, allowing uncertainty in petrophysical parameters, and expanding the size of ResUNet++ to the practical reservoir extent.

Summary The reduction of pore pressure caused by depletion can induce significant reservoir compaction and loss of permeability, especially in unconsolidated reservoirs. In this paper, we develop a numerical approach on the basis of computer-based simulations of unconsolidated rock samples subjected to mechanical tests that replicate the one-dimensional (1D) strain depletion path and allow for a prediction of permeability loss. The 1D strain stress path is a good approximation for long and thin conventional reservoirs with a compliant caprock. The numerical sample consists of crushable stiff and soft grains (proxies for sand and shale) simulated with the discrete element method (DEM) coupled with the bonded-particle model (BPM). Model parameters are calibrated through numerical single-grain-crushing tests which reproduce the experimentally measured sand strength. Grain crushing induced by the uniaxial strain stress path results in a pronounced reduction of porosity and permeability, which manifests more readily for samples with large grain size. The change of particle-size distribution indicates that high effective stresses cause grain crushing and production of a significant amount of fines that lower permeability. Simulation results indicate that the presence of soft grains and inclusions (e.g., shale fragments) facilitates grain crushing. Reservoir simulations—incorporating the change of porosity and permeability as a compaction table—show that the compaction can enhance cumulative production due to compaction drive but also reduces production rate by impairing the reservoir permeability. This multiscale numerical workflow bridges grain-scale compaction behavior and field-scale reservoir production.

Summary Implementation of a drift–flux (DF) multiphase–flow model within a fully coupled wellbore/reservoir simulator is nontrivial because it must adhere to a number of strict requirements to ensure numerical robustness and convergence. The existing DF model that meets these requirements is only fully posed from 2° (from the horizontal) to upward vertical. Our work attempts to extend the current DF model such that it is numerically robust, accurate, and applicable to all well inclinations. To gauge accuracy, model parameterization used 5,805 experimental data points from a well–established data set, along with a second data set comprising 13,440 data points extracted from the OLGA–S library (Schlumberger 2017b). Forecast accuracy of the proposed model is compared with that of two state–of–the–art DF models (applicable to all inclinations but unsuited for coupled simulation), and it exhibits equivalent or better performance. More significantly, the model is shown to be numerically smooth, continuous, and stable for cocurrent flow when implemented in a fully implicit and coupled wellbore/reservoir simulator.

The successful modeling of wellbore heat exchangers requires tight coupling between the formation heat conduction and the wellbore thermal dynamics. In this paper, we developed three representative solutions: a fully numerical solution that treats both formation and wellbore numerically, a semi-numerical solution that treats formation analytically and wellbore numerically and a fully analytical solution that treats both formation and wellbore analytically. The outlet temperature, pressure, and heat flow rate predicted by the three solutions are compared. The results of this comparison indicate that the fully numerical solution is the only accurate solution for the early transient time. After the system reaches quasi-steady state, temperature and heat flow estimated from all three solutions are in very good agreement. The analysis of transient fluid properties and mass flow rates revealed that constant fluid property model cannot accurately estimate the pressure loss for the system. The newly proposed fully analytical solution is applied in an economic analysis problem for calculating the optimal insulation thickness and mass flow rate to minimize the cost of generated power.

Fast assimilation of monitoring data to update forecasts of pressure buildup and carbon dioxide (CO2) plume migration under geologic uncertainties is a challenging problem in geologic carbon storage. The high computational cost of data assimilation with a high-dimensional parameter space impedes fast decision-making for commercial-scale reservoir management. We propose to leverage physical understandings of porous medium flow behavior with deep learning techniques to develop a fast data assimilation-reservoir response forecasting workflow. Applying an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates geologic properties and predicts reservoir performance with quantified uncertainty from pressure history and CO2 plumes interpreted through seismic inversion. As the most computationally expensive component in such a workflow is reservoir simulation, we developed surrogate models to predict dynamic pressure and CO2 plume extents under multi-well injection. The surrogate models employ deep convolutional neural networks, specifically, a wide residual network and a residual U-Net. The workflow is validated against a flat three-dimensional reservoir model representative of a clastic shelf depositional environment. Intelligent treatments are applied to bridge between quantities in a true-3D reservoir model and those in a single-layer reservoir model. The workflow can complete history matching and reservoir forecasting with uncertainty quantification in less than one hour on a mainstream personal workstation.

Automatic and early detection of flow influx during drilling is important for improving well-control safety. In this paper, a new method that can automatically analyze real-time drilling data and detect the flow influx event is presented. The new method combines the physics-based dimension reduction and time-series data mining approaches. Two kick indicators are defined, representing the drilling parameter group (DPG) and flow parameter group (FPG), respectively. Additionally, two real-time trend-analysis methods, the divergence of moving average (DMA), and the divergence of moving slope average (DMSA) are applied to quantify trend evolutions of the two indicators. The kick event is identified based on the anomalous trends held by the two kick indicators. A final kick-risk index (KRI) is calculated in real time to indicate the probability of kick events and to trigger the alarm. The method is tested against four offshore kick events. With KRI threshold setting as 0.8, the average detection time is 64% less than the reported detection time. The application of DPG kick indicator allows the early kick detection without additional downhole sensors or costly flow meters.

Depletion-induced formation compaction is a concern for production in hydrocarbon reservoirs. In this study, numerical mechanical tests and digital samples are proposed as an alternative to uniaxial strain experiments for investigating the effects of compaction. We propose a grain crushing model with two-sized subgrains based on the discrete element method and bonded-particle model (DEM-BPM) and perform numerical single grain crushing tests to calibrate the model parameters against the experimental sand grain characteristic strength. Numerical uniaxial strain tests are used to predict porosity and permeability reduction caused by reservoir compaction. Grain crushing incurs a significant reduction of porosity under the uniaxial strain stress path, which becomes more evident for digital samples consisting of larger grains. The stress-dependent changes in porosity and permeability obtained from the DEM simulations can be used as inputs for a reservoir-scale correlation to predict the productivity loss due to reservoir compaction.

SummaryWell interference is a common phenomenon in unconventional-reservoir development. The completion and production of infill wells can lead to either positive or negative well-interference impacts on the existing producers. Many researchers have investigated the well-interference phenomenon; however, few of them attempted to apply rigorous simulation methods to analyze both positive and negative well-interference effects, especially in two different formations. In this work, we develop a comprehensive compositional reservoir model to study the well-interference phenomena in the Eagle Ford Shale/Austin Chalk production system. The reservoir model considers capillary pressure in the vapor/liquid-equilibrium (VLE) equation (nanopore-confinement effect), and applies the embedded discrete-fracture model (EDFM) for dynamic fracture modeling. We also include a multisegment-well model to characterize the wellbore-crossflow effect introduced by fracture hits. The simulation results indicate that negative well-interference impact is much more common in the production system. With a smaller permeability difference, the hydraulic-fracturing effect can lead to a positive well-interference period of several hundred days. The nanopore-confinement effect in the Eagle Ford Shale can contribute to the negative well-interference effect. We also analyze the impact of other factors such as initial reservoir pressure, matrix porosity, initial water saturation, and the natural-fracture system on the well performance. Our work provides valuable insights into dynamic well performance under the impact of well interference.

Summary Liquid loading is a challenging issue in most mature gas fields. The dynamic interaction between wellbore and reservoir when liquid loading happens cannot be comprehensively simulated by a single wellbore simulator or a single reservoir simulator. In this paper, we develop a fully implicitly coupled wellbore/reservoir model to characterize the flow transients in liquid-loaded horizontal gas wells. We fully couple a wellbore model with an in-house reservoir simulator based on the control-volume finite-difference method. Wellbore transient material-balance equations and mixture momentum-balance equations are solved simultaneously with the reservoir equations to obtain pressure, mixture velocity, and phase holdup in each wellbore segment. Also, we propose a modified drift-flux model that is capable of predicting the flow-regime transition for different pipe inclinations from vertical to horizontal. The modified drift-flux model is integrated in the coupled wellbore/reservoir simulator to characterize the two-phase flow in horizontal wellbores. We validate the coupled wellbore/reservoir model with a commercial multisegment wellbore (MSW)/reservoir simulator. The revised drift-flux formulation not only matches a commercial simulator in production forecast and wellbore pressure, but also predicts the subsequent unstable liquid production caused by flow-regime transitions. For a synthetic field-scale case, the new model predicts gas production that lasts 23 days longer than the prediction of a commercial simulator. This paper extends the capability of a fully implicitly coupled wellbore/reservoir simulator to simulate the transient liquid-loading phenomenon. The model can serve as a promising tool for gasfield development.

Shale gas reservoir is comprised of highly heterogeneous porosity systems including hydraulic/secondary fractures, inorganic and organic matrix. Multiple non-Darcy flow mechanisms in the shale matrix further bring challenges for modeling. In this paper, we developed a framework combining a multi-physics compositional simulator with Multi-Porosity Modeling preprocessor for gas storage and transport in shale. A Triple-Porosity Model is used to characterize the three porosity systems in shale gas reservoirs. In the fracture porosity the heterogeneous impact of secondary fractures distribution on matrix-to-fracture fluid transfer is revealed by shape factor distribution. They are upscaled with superior accuracy from a detailed Discrete Fracture Network Model (DFN) sector model, where orthogonal hydraulic fractures are explicitly discretized. With the occurrence of nano-pores in shale matrix, the interaction between pore-wall and gas molecules is considered via Knudsen diffusion and gas slippage. Gas adsorption on the pore-wall of organic matrix is modeled by extended Langmuir isotherm. The inter-porosity and intra-porosity connectivities in the Triple-Porosity Model are flexibly controlled by arbitrary connections. Our results show that gas production in the Triple-Porosity Model with shape factor upscaled from DFN exhibits different production performance from models with uniform shape factor distribution. The deviations are caused by the dominance of different regions at different production periods. Connection topology in the shale gas reservoir is also comprehensively assessed. We demonstrate that the intra-porosity connections in the inorganic and organic matrix have negligible impact on the global gas flux, while the inter-porosity connections have different levels of importance for the gas production. Moreover, different combinations of flow and storage mechanisms are investigated. We show that Langmuir desorption maintains reservoir pressure, but gas slippage and Knudsen diffusion accelerate the pressure drop. Both mechanisms contribute to improve the gas production and the consideration of them simultaneously improve gas production most.

SummaryFracture-density evaluation has always been challenging for the petroleum industry, although it is a required characteristic for reliable reservoir characterization. Production can be directly controlled by fracture density, especially in tight reservoirs. Previous publications showed that use of high thermal neutron-capture cross-sectional (HTNCC) contrast agents can enhance the sensitivity of neutron logs to the presence of fractures. However, all these studies focus on locating the proppants. In this paper, we introduce a method of injecting electrically transported charged boron carbide (B4C) contrast agents to naturally fractured formations to enhance the propagation of the contrast agents into the secondary-fracture (natural and induced) network by use of an externally applied electric field and to characterize the fracture density in the unpropped region by use of the enhanced neutron porosity logs.We perform numerical simulations to validate the feasibility of the proposed technique. A physical model derived from electrophoretic velocity and material-balance formulations is proposed and solved to simulate the spatial distribution of contrast agents. Furthermore, we simulate neutron porosity logs by solving the neutron-diffusion equation, which allows a fast analysis for the proposed technique.The simulation results confirmed that an external electric field can significantly enhance the transport of charged contrast agents into the secondary-fracture network. Sensitivity analysis revealed that increasing particle ζ-potential can efficiently decrease the transport time. Furthermore, we applied the introduced technique on synthetic cases with variable secondary-fracture density ranging from 1 to 8%. The relative variation in the simulated neutron porosity before and after applying the electric potential field was up to 50% in a formation with 8% fracture density after applying an electric field for 6 hours. The proposed technique can potentially enable application of neutron porosity logs in fracture characterization, including assessment of secondary-fracture density, if combined with other well logs.

The topic of fracture complexity is commonly evoked when discussing hydraulic fracturing of unconventional reservoirs. In this context, it is typically considered beneficial to successful stimulation, as it provides increased surface area, relative to single planar fractures. However, in the near-wellbore region (NWR), this same fracture complexity, commonly referred to as tortuosity, can be detrimental to successful placement of fluid and proppant. In the extreme, if not properly identified and mitigated, fracturing stages may need to be abandoned which leads to unstimulated sections of the wellbore and reduced completions efficiency. Yet, the ability to adequately quantify this phenomenon during stimulation remains limited. In this paper, we show how modern diagnostic techniques can be leveraged to provide insight into this critical region. Specifically, we combine interpretations from both fiber optic distributed acoustic sensing (DAS) and external downhole pressure gauges (BHG) to improve the characterization of the NWR. This project was executed during the stimulation of a horizontal well located in the Wolfcamp formation within the Midland Basin. We first review observations from the external cemented in place fiber and the external pressure gauges. The second section presents an investigation of fracturing net pressures trends identified with external pressure gauges. We apply traditional Nolte-Smith fracture diagnostics to analyze fracture propagation and near-wellbore proppant dynamics. The net-pressure investigation reveals that even in unconventional reservoirs, Nolte-Smith diagnostic plots are applicable, when external pressure gauges are available. We show that near-wellbore proppant screen-outs identified by the Nolte-Smith plot are independently identified by Distributed Acoustic Sensing (DAS) data. In the third section we have attempted to develop a process to quantify near-wellbore tortuosity, where machine learning (ML) algorithm(s) were utilized to estimate the friction pressure induced by near-wellbore tortuosity. The training, testing and validation needed for machine learning algorithm(s) were based on utilizing DAS data, downhole gauge data, pumping schedule and post fracturing reports. The studies indicate that friction pressure due to tortuosity is initially high within the transient rate period and decreases to stable values later within the stage. The validation studies show promising performance of ML algorithm(s) for near-wellbore friction pressure estimation, even without downhole gauge data as inputs. It is expected that with further development of ML algorithms needing limited training data shall allow development of diagnostic tools for better prediction of bottom hole treating pressures in wells without the need of acquiring high frequency downhole data. The paper also makes an attempt to validate the application of Nolte-Smith plot in unconventional reservoirs, especially in characterizing the NWR. Additionally, fluid communication between stages highlights the importance of the NWR on ensuring stage isolation. Finally, the applied ML algorithm for near-wellbore tortuosity pressure estimation is shown to have a reasonable generalization performance, which may serve as a diagnostic tool for completion optimization.

Implementation of a drift-flux (DF) multiphase flow model within a fully-coupled wellbore-reservoir simulator is non-trivial and must adhere to a number of strict requirements in order to ensure numerical robustness and convergence. The existing DF model that can meet these requirements is only fully posed for upward flow from 2 degrees (from the horizontal) to vertical. The work attempts to extend the current DF model to a unified and numerically robust model that is applicable to all well inclinations. In order to achieve this objective, some 5805 experimentally measured data points from 22 sources as well as 13440 data points from the OLGA-S library are utilized to parameterize a new DF model – one that makes use of the accepted upward flow DF model and a new formulation extending this to horizontal and downward flow. The proposed model is compared against 2 existing DF models (also applicable to all inclinations) and is shown to have better, or equivalent, performance. More significantly, the model is also shown to be numerically smooth, continuous and stable for co-current flow when implemented in a fully implicitly coupled wellbore-reservoir simulator.

Liquid loading is an inevitable production issue for mature gas fields. A varied group of phenomena after the onset of liquid loading has been observed in field, including the natural cyclical production of liquid loaded wells. Most previous modeling studies focused on predicting the onset of liquid loading. The production behaviors after the onset of liquid loading is equally important and requires strict modeling techniques to simulate dynamic interactions between wellbores and reservoirs.In this paper, we apply a newly developed fully implicitly coupled well-reservoir simulator to systematically investigate the well behavior after the onset of liquid loading. The model honors the mass and momentum balances in both reservoir and wellbore systems, and thus allows us to analyze both wellbore and reservoir dynamics after the onset of liquid loading. The simulation results indicate that there exist a gas-water coproduction period and a zero liquid production period after the onset of liquid loading. For reservoir permeability as low as 0.1md, the liquid-loaded horizontal well might experience natural cyclical production after the onset of liquid loading, which coincides with field observations. Both uniform stimulation and multi-stage hydraulic fractures help mitigate the production phenomenon. The near-wellbore reservoir pressure build up and wellbore fluid reinjection are also evaluated. This work demonstrates the successful application of the coupled wellbore reservoir model in predicting the rich production phenomena of stimulated horizontal wells after the onset of liquid loading.

A major part of the uncertainty for shale reservoirs comes from the distribution and properties of the fracture network. However, explicit fracture models are rarely used in uncertainty quantification due to their high computational cost. This paper presents a workflow to match the history of reservoirs with complex fracture network with a novel forward model. By taking advantage of the efficiency of the model, fractures can be explicitly characterized, and the corresponding uncertainty about the distribution and properties of fractures can be evaluated. No upscaling of the fracture properties is necessary, which is usually a required step in a traditional workflow. The embedded discrete fracture model (EDFM) has recently been studied by many researchers due to its high efficiency compared to other explicit fracture models. By assuming a linearly distributed pressure near fractures, EDFM can provide a sub-grid resolution that lifts the requirement to refine near the fractures to a comparable size as the fracture aperture. Although efficient, considerable error is reported when applying this method to simulate flow barriers, especially when dominant flux direction is across instead of along the fractures. In this work, a novel discrete fracture model, compartmental EDFM (cEDFM) is developed based on the original EDFM framework. However, different from the original method, in cEDFM the fracture would split matrix grid blocks when intersecting them. The new model is benchmarked for single phase as well as multi-phase cases, and the accuracy is evaluated by comparing to fine explicit cases. Results indicate the improved model yields much better accuracy even for multi-phase flow simulation with flow barriers. In the second part of the work, we applied the model in history matching and performed uncertainty quantification to the fracture network for two synthetic cases. We used Ensemble Kalman Filter (EnKF) as the data assimilation algorithm due to its robustness for cases with large uncertainty. The initial state does not need to be close to the truth to achieve convergence. Also EnKF performs well for the history matching of reservoirs with complex fracture network, where the number of parameters can be large. Therefore, it is advantageous compared to using Ensemble Smoother (ES) or Markov Chain Monte Carlo (MCMC) for fractured reservoirs. After the final step of data assimilation, a good match is obtained that can predict the production reasonably well. The proposed cEDFM model shows its robustness to be incorporated into the EnKF workflow, and benefit from the efficiency of the model, this work made it practical to perform history matching with explicit fracture models.

The reduction of pore pressure caused by depletion can induce significant reservoir compaction, especially in unconsolidated reservoirs. Experiments using unconsolidated core samples are often sparse and costly. We develop a numerical approach based on computer-based simulations of rock samples and mechanical tests. The numerical sample consists of crushable grains simulated with the discrete element method (DEM) and the bonded-particle model (BPM). Model parameters are calibrated through numerical single-grain-crushing tests which reproduce the experimentally-measured sand strength. Grain crushing induced by the uniaxial strain stress path results in a pronounced reduction of porosity and permeability, which manifests more readily for samples with large grain size. The change of particle size distribution indicates that the high effective stress causes grain crushing and produces a significant amount of fines. We perform numerical uniaxial strain tests on numerical samples comprising stiff and soft mineral grains. Simulation results indicate that the presence of soft grains and inclusions (e.g. shale fragments) facilitates the grain crushing. Reservoir simulations, incorporating the change of porosity and permeability as a compaction table, show that the upscaled compaction can enhance production due to compaction drive but also reduces production rate by impairing the reservoir permeability. This multiscale numerical workflow bridges particle-scale compaction behavior and field-scale reservoir production. In this paper, (a) DEM simulations provide a useful tool to investigate compaction effects and complement laboratory experiments; (b) the multi-scale numerical approach can predict the depletion-induced evolution of reservoir production.

High water-cut has been observed for many multi-fractured horizontal wells (MFHWs) in China soon after waterflooding begins. Available well-testing models of single well ignored the effect of adjacent wells on the MFHW, and they are unable to evaluate whether MFHW (producer) and surrounding vertical wells (injectors) are in good pressure communication. To fill this gap, this work presents a multi-well interference testing (MWIT) model to consider the interference of injectors and further match the interference pressure data. The MWIT model is established to investigate the effect of multiple injection wells on transient-pressure behavior of the MFHW. Due to the interferences from injectors, the pressure and pressure-derivative curves of MWIT move down beginning with the biradial flow regime for single MFHW model, and pseudo-radial flow (horizontal line with the value of 0.5 on pressure-derivative curve) disappears. Sensitivity analysis was conducted to discuss the effects of crucial parameters on the pressure response, including total injection rates, unequal injection rates of injectors, well spacing, injector distribution, number and production of hydraulic fractures. When total injection rates are lower than the production rate, the pressure derivative will eventually stabilize at 0.5*(1-Σ(qIncjD)) during the interference-flow regime on the log-log type curves. Since only the positive number can be shown in the log-log graph, semi-log curves are also developed to fully characterize the flow regimes of MWIT. A novel finding is that pressure derivative also ultimately behave as a horizontal line with the value of 0.5*(1-Σ(qIncjD)) when total injection rates are equal or higher than production rates on the semi-log curves. The total injection rates and well spacing between the MFHW and injectors have a significant effect on middle and late pressure behaviors, whereas the number and production of fractures mainly affects the pressure responses during early to middle period. Type curves indicate that the effect of surrounding injectors are significant and cannot be ignored, and the novel characteristics provide potential application of the MWIT model to estimate formation parameters. Case studies highlight the application of the proposed method in effectively matching the interference pressure data. Interference-testing analysis of the MWIT provides a better reservoir evaluation compared to single-well testing model.

Liquid loading is a challenging issue in most mature gas fields. The dynamic interaction between wellbore and reservoir when liquid loading happens cannot be comprehensively simulated by a single wellbore simulator or a single reservoir simulator. In this paper, we developed a fully implicitly coupled wellbore/reservoir model to predict the onset of liquid loading and simulate the subsequent unstable production period. We fully couple a multi-segment well model with an in-house compositional reservoir simulator based on the control-volume finite-difference method. Transient material balance and mixture momentum balance are solved simultaneously with the reservoir system to obtain pressure, mixture velocity, and phase holdup in each wellbore segment. We further proposed a drift-flux model that is able to predict the transitions from stable flow patterns to unstable patterns for different pipe inclinations from vertical to horizontal. The model is applied in the coupled wellbore-reservoir simulator to characterize the two-phase flow in horizontal wellbores. We validate the coupled wellbore-reservoir model with a numerical reservoir simulator and demonstrate the model capability in simulating the whole producing life of liquid-loaded horizontal gas wells. The parametric study indicates that wellbore perforation numbers and joint pipe length before the first perforation can influence the onset time of liquid loading and the duration of unstable production. Most of previous studies focus on predicting the onset of liquid loading phenomenon. In this paper, our model first achieves the analysis of the unstable production period after liquid loading occurs. The detection of unstable production can be much straightforward comparing to the onset of liquid loading, which makes the model a promising tool in assisting the decision of deliquification strategies for field operators.

Drilling operation involves significant heat-transfer between the drilling fluid, downhole tubulars, and surrounding formation. In deepwater assets, fluid circulation through tubulars submerged in cold seawater, making the problem more complex. Estimating temperatures in the wellbore during and after circulation is critical for mud properties, downhole thermal stress, flow assurance, and gas detection purpose. While numerical simulation can be used to estimate fluid temperatures during deepwater drilling, such simulations are complicated and time-consuming. This paper presents a novel analytical model to estimate temperature profiles during drilling circulation in deepwater environment. The Energy balance is set up over a small differential control volume along the wellbore to develop the model. An analytical solution for estimating the drill pipe and annulus fluid circulation temperatures is presented. For the shut-in period, that often follows drilling, a transient shut-in temperature model is improved and coupled with the drilling circulation model. The end of circulation fluid temperature is used as the initial condition for the shut-in period, allowing shut-in temperature estimation as a function of time. An offshore gas well, that is instrumented with Multi-Distributed Temperature Sensors (MDTS), offers accurate temperature data that is used to validate the model.

Shale gas reservoir is comprised of highly heterogeneous porosity systems including hydraulic/secondary fractures, inorganic and organic matrix. Multiple non-Darcy flow mechanisms in the shale matrix further bring challenges for modeling. In this paper, we developed a framework combining a multi-physics compositional simulator with Multi-Porosity Modeling preprocessor for gas storage and transport in shale. A Triple-Porosity Model is used to characterize the three porosity systems in shale gas reservoirs. In the fracture porosity the heterogeneous impact of secondary fractures distribution on matrix-to-fracture fluid transfer is revealed by shape factor distribution. They are upscaled with superior accuracy from a detailed Discrete Fracture Network Model (DFN) sector model, where orthogonal hydraulic fractures are explicitly discretized. With the occurrence of nano-pores in shale matrix, the interaction between pore-wall and gas molecules is considered via Knudsen diffusion and gas slippage. Gas adsorption on the pore-wall of organic matrix is modeled by extended Langmuir isotherm. The inter-porosity and intra-porosity connectivities in the Triple-Porosity Model are flexibly controlled by arbitrary connections. Our results show that gas production in the Triple-Porosity Model with shape factor upscaled from DFN exhibits different production performance from models with uniform shape factor distribution. The deviations are caused by the dominance of different regions at different production periods. Moreover, different combinations of flow and storage mechanisms are investigated. We show that Langmuir desorption maintains reservoir pressure, but gas slippage and Knudsen diffusion accelerate the pressure drop. Both mechanisms contribute to improve the gas production and the consideration of them simultaneously improve gas production most.

Fracture density evaluation has always been challenging for the petroleum industry, although it is a required characteristic for reliable reservoir characterization. Production can be directly controlled by fracture density, especially in tight reservoirs. Previous publications introduced the method of using high thermal neutron capture cross-section contrast agents like boron carbide (CB4) to enhance the sensitivity of neutron logs to the presence of fractures. However, all of the studies focus on locating the proppants. In this paper, we propose to (a) enhance the propagation of the contrast agents into the secondary (natural and induced) fracture network using an externally applied electric field and (b) characterize the fracture density in un-propped area using the enhanced neutron porosity logs. We perform numerical simulations to validate the feasibility of the proposed technique. A physical model derived from electrophoretic velocity and material balance formulations is proposed and solved to simulate the spatial distribution of contrast agents. Furthermore, we simulate neutron porosity logs by solving the neutron diffusion equation which significantly reduces the computing cost (from hours to a few seconds) compared to conventional use of the MCNP (Monte Carlo N-Particle Code) technique. The simulation results confirmed that an external electric field can significantly enhance the transport of charged contrast agents into the secondary fracture network. Sensitivity analysis revealed that increasing particle zeta potential can efficiently decrease the transport time. Furthermore, we applied the introduced technique on synthetic cases with variable secondary fracture density ranging from 1% to 8%. The relative change of simulated neutron porosity before and after applying the electric potential field was up to 50% in a formation with 8% fracture density after applying an electric field for 6 hours. The proposed technique can potentially enable application of neutron porosity logs in fracture characterization, including assessment of secondary fracture density, if combined with other well logs.

Permeability variation in tight channel sand formation makes an important role in gas production. Based on the features of channel sand formation, a mathematical model has been established considering anisotropy of permeability. The analytical solutions were derived for productivity of both vertical wells and vertically fractured wells. Simulation results show that, gas production rate of anisotropic channel sand formation is less than that of isotropic formation. For vertically fractured well, artificial fracture direction, drainage radius, permeability ratio and fracture half-length have considerable influence on production rate. The optimum fracture direction should be deviated less than π/8 from the maximum permeability direction (or the channel direction). In addition, the analytical model was verified by in situ measured data. The research provides theoretical basis for the development of tight channel sand gas reservoirs.