Elahi, Seyed Moein (Department of Chemical and Petroleum Engineering, University of Calgary) | Ahmadi Khoshooei, Milad (Department of Chemical and Petroleum Engineering, University of Calgary) | Scott, Carlos E. (Department of Chemical and Petroleum Engineering, University of Calgary) | Carbognani Ortega, Lante (Department of Chemical and Petroleum Engineering, University of Calgary) | Chen, Zhangxin (Department of Chemical and Petroleum Engineering, University of Calgary) | Pereira-Almao, Pedro (Department of Chemical and Petroleum Engineering, University of Calgary)
Simultaneous in-reservoir upgrading and recovery of heavy oil are experimentally studied by using a continuous setup filled with carbonate cores. Upgrading reaction products as well as the recovered oil are analyzed in order to investigate the recovery mechanisms associated with this process.
In-situ upgrading technology (ISUT) is based on injection of high molecular weight (low quality) cut of oil, e.g., vacuum residue (VR), together with ultradispersed nano-catalyst and hydrogen. By injecting VR, catalyst, and hydrogen, the catalytic nano-particles deposit in the rock around an injection well, where the upgrading reactions occur. In this study, first, upgrading reactions happen inside a core packed container at the temperature, pressure, and residence time of 360 °C, 10 MPa, and 36 h, respectively. Subsequently, the hot reaction products are directed into another cylinder filled with carbonate cores to displace the heavy oil in place.
There are two main steps in a reservoir during ISUT. First, the injected VR is converted to lighter products during hydroprocessing reactions. Then the upgraded liquid and gaseous products along with hydrogen will displace the heavy oil toward production wells. At the conditions of this experiment in the reactor, 47 wt% of the VR cut is converted to lighter products with 1 wt% gases (mainly H2S and hydrocarbons with 1 to 5 carbon atoms) and 5.4 wt% naphtha cut (hydrocarbons with 5 to 12 carbon atoms). These light products act as solvents in the areas farther from the reaction zone and enhance the recovery of heavy oil. In addition, high temperatures, mechanical push, and rock matrix thermal expansion improve the oil displacement in the carbonate rock.
By enhancing the oil recovery and permanently upgrading heavy oil in one single stage, the need for diluent addition and steam generation is minimized, which makes ISUT economical and environmentally favorable. In an innovative experimental setup, both upgrading and recovery steps in the ISUT process are carefully analyzed.
Since decades, steam-assisted oil recovery processes have been successfully deployed in heavy oil reservoirs to extract bitumen/heavy oil. Current resource allocation practices mostly involve reservoir model-based open loop optimization at the planning stage and its periodic recurrence. However, such decades-old strategies need a complete overhaul as they ignore dynamic changes in reservoir conditions and surface facilities, ultimately rendering heavy oil production economically unsustainable in the low-oil-price environment. Since steam supply costs account for more than 50% of total operating costs, a data-driven strategy that transforms the data available from various sensors into meaningful steam allocation decisions requires further attention.
In this research, we propose a purely data-driven algorithm that maximizes the economic objective function by allocating an optimal amount of steam to different well pads. The method primarily constitutes two components: forecasting and nonlinear optimization. A dynamic model is used to relate different variables in historical field data that were measured at regular time intervals and can be used to compute economic performance indicators (EPI). The variables in the model are cumulative in nature since they can represent the temporal changes in reservoir conditions. Accurate prediction of EPI is ensured by retraining regression model using the latest available data. Then, predicted EPI is optimized using a nonlinear optimization algorithm subject to amplitude and rate saturation constraints on decision variables i.e., amount of steam allocated to each well pad.
Proposed steam allocation strategy is tested on 2 well pads (each containing 10 wells) of an oil sands reservoir located near Fort McMurray in Alberta, Canada. After exploratory analysis of production history, an output error (OE) model is built between logarithmically transformed cumulative steam injection and cumulative oil production for each well pad. Commonly used net-present-value (NPV) is considered as EPI to be maximized. Optimization of the objective function is subject to distinct operating conditions and realistic constraints. By comparing results with field production history, it can be observed that optimum steam injection profiles for both well pads are significantly different than that of a field. In fact, the proposed algorithm provides smooth and consistent steam injection rates, unlike field injection history. Also, the lower steam-oil ratio is achieved for both well pads, ultimately translating into ~19 % higher NPV when compared with field data.
Inspired from state-of-the-art control techniques, proposed steam allocation algorithm provides a generic data-driven framework that can consider any number of well pads, EPIs, and amount of past data. It is computationally inexpensive as no numerical simulations are required. Overall, it can potentially reduce the energy required to extract heavy oil and increase the revenue while inflicting no additional capital cost and reducing greenhouse gas emissions.
This study is based on the premise that most of the trapped hydrocarbons can be produced, if we substitute them with another ‘acrificial’ fluid that has amplified interactions with organic pore walls, such as CO2. For the presented study, a downhole shale sample is analyzed in the laboratory to predict gas storage properties such as pore-volume, pore compressibility, and gas adsorption capacity. Then a series of pressure pulse decay measurements are performed to delineate transport mechanisms and predict stress-sensitive permeability. These coefficients are obtained as the calibration parameters of a simulation-based optimization for injection and production. Simulation model considers compositional gas flow in a deformable porous media and includes a multi-continuum porosity, with organic and inorganic pores, and micro-fractures. The experimental and simulation results show that most of the injected CO2 is adsorbed in the organic matrix and are not produced back. This is because CO2 molecules have significantly larger adsorption capacity when compared to methane. The strong adsorption of CO2 improves the release of natural gas from kerogen pores. This indicates that the separation of produced CO2 will be a minimal cost. Transport in kerogen has significant pore wall effects, and includes large mass fluxes of the adsorbed molecules by the walls due to surface diffusion. In essence, the adsorbed CO2 molecules significantly influence transport of methane. The results also show core-plug permeability is stress-sensitive due to presence of micro-fractures. Forward simulation results using optimum parameters indicate that closure stress developing near the fractures could significantly control the volume of CO2 injected. This raises operational issues on when to start injecting, and how to inject CO2. Using a simulation study of a production well with single-fracture, we show that fracture closure stress develops rapidly and production rate becomes a slave of the fracture geo-mechanics, e.g., strength of the proppants and the level of proppant embedment.
Lin, Ran (Southwest Petroleum University) | Ren, Lan (Southwest Petroleum University) | Zhao, Jinzhou (Southwest Petroleum University) | Tao, Yongfu (Exploration and Development Research Institute, Yumen Oilfield Company) | Tan, Xiucheng (Southwest Petroleum University) | Zhao, Jiangyu (Southwest Petroleum University)
Multi-stage & multi-cluster fracturing in horizontal well drilling is the core technology in for commercial exploitation of shale gas resevoir. According to vast field data, there is remarkable positive correlation relationship between stimulated reservoir volume (SRV) and shale gas production. Hence, estimating the SRV is essential for both pre-fracturing design and post-fracturing evaluation. However, the forming process of SRV involves with many complex mechanisms, making it is difficult to be simulated.
In this paper, we establish a mathematical model to estimate the SRV by simulating multiple hydraulic fractures propagate, formation stress change and reservoir pressure rise; consequently, the stress and pressure change might make natural fractures occur tensile failure or shear failure, generating a high-conductivity zone (i.e., SRV) in the shale reservoir.
To solve the model, displacement discontinuity method (DDM) is applied to simulate non-planar propagation of multiple hydraulic fractures and calculate formation stress change. Finite difference method (FDM) is used to compute reservoir pressure rise. The natural fractures failure state is determined by tensor formulae derived from Warpinski's failure theory. This SRV estimation method involves a variety of complex but crucial physical mechanisms during shale fracturing process which include unequal flow-rate distribution in different hydraulic fractures, non-planar hydraulic fractures propagation under stress interference, reservoir permeability increases with SRV expanding, two types of natural fracture failure and so on.
A field case study was performed to show the dynamic processes of hydraulic fractures propagation, reservoir permeability increase, and the SRV expansion during shale gas fracturing. Then we compared the simulation results with analytical solution, published papers and on-site microseismic monitoring data to verify our model. Finally, the influence of geological condition and engineering parameters on SRV was investigated by sensitivity analysis.
The reporting of potential resources is essential to assess the future development plan and profitability of a petroleum discovery, but if the project is under appraised and production data are absent, analysts often use analogs for preliminary estimates of technically recoverable volumes. To address this, a workflow is presented for selecting appropriate analogs for unconventional plays and using them to estimate the target play's potential. The proposed technique is demonstrated with a case study of the as-yet undeveloped Bowland Shale, which is the most prominent of the shale plays in the United Kingdom (UK) and is at the early stage of its assessment. The paper describes the current shale gas activity in the UK, highlighting the enviromental constraints placed on would-be Bowland Shale developers, which impact on drilling and production operations and stem from the geographic proximity of urban developments, infrastructure and nature, which limit the size of well pad footprint in the UK where land use is high. Studies have estimated the play's in-place resources for possible future development, but there are few estimates of its corresponding recoverable volumes due to lack of production history. At the outset, a database is created with published minimum-average-maximum ranges of key parameters such as total organic carbon, maturity level, gas filled porosity, permeability, etc. that play a major role in resources estimation and recovery potential for all unconventional plays. A comparison of triangular distributions, key parameter by key parameter, between the target shale play and the analog database, is then carried out using novel graphical and statistical methods to establish a "confidence factor" relating to the analog's viability. The most appropriate analog for the Bowland Shale is chosen from an exhaustive list of North American shale gas plays. Analytical approaches are then used to transform a model of the published type well performance of the selected analog by exchanging key model parameters with those of the target shale play. The paper shows how UK operational constraints can be statistically incorporated into the workflow and have a marked effect on the estimated recovery from the Bowland Shale.
Berawala, Dhruvit Satishchandra (Department of Energy and Petroleum Technology, University of Stavanger, Norway and The National IOR Centre of Norway) | Østebø Andersen, Pål (Department of Energy Resources, University of Stavanger, Norway and The National IOR Centre of Norway)
Only 3-10 % of gas from tight shale is recovered economically through natural depletion, demonstrating a significant potential for enhanced shale gas recovery (ESGR). Experimental studies have demonstrated that shale kerogen/organic matter has higher affinity for CO2 than methane, CH4, which opens possibilities for carbon storage and new production strategies.
This paper presents a new multicomponent adsorption isotherm which is coupled with a flow model for evaluation of injection-production scenarios. The isotherm is based on the assumption that different gas species compete for adsorbing on a limited specific surface area. Rather than assuming a capacity of a fixed number of sites or moles this finite surface area is filled with species taking different amount of space per mole. The final form is a generalized multicomponent Langmuir isotherm. Experimental adsorption data for CO2 and CH4 on Marcellus shale are matched with the proposed isotherm using relevant fitting parameters. The isotherm is first applied in static examples to calculate gas in place reserves, recovery factors and enhanced gas recovery potential based on contributions from free gas and adsorbed gas components. The isotherm is further coupled with a dynamic flow model with application to CO2-CH4 substitution for CO2-ESGR. We study the feasibility and effectiveness of CO2 injection in tight shale formations in an injection-production setting representative of lab and field implementation and compare with regular pressure depletion.
The production scenario we consider is a 1D shale core or matrix system intitally saturated with free and adsorbed CH4 gas with only left side (well) boundary open. During primary depletion, gas is produced from the shale to the well by advection and desorption. This process tends to give low recovery and is entirely dependent on the well pressure. Stopping production and then injecting CO2 into the shale leads to increase in pressure where CO2 gets preferentially adsorbed over CH4. The injected CO2 displaces, but also mixes with the in situ CH4. Restarting production from the well then allows CH4 gas to be produced in the gas mixture. Diffusion allows the CO2 to travel further into the matrix while keeping CH4 accessible to the well. Surface substitution further reduces the CO2 content and increases the CH4 content in the gas mixture that is produced to the well. A result of the isotherm and its application of Marcellus experimental data is that adsorption of CO2 with resulting desorption of CH4 will lead to a reduction in total pressure if the CO2 content in the gas composition is increased. That is in itself an important drive mechanism since the pressure gradient driving fluid flow is maintained (pressure buildup is avoided). This is a result of CO2 being found to take ~24 times less space per mol than CH4.
Rate and pressure transient analysis is considered a routine process that has been developed and refined over many years. The underlying assumptions of linearity justify the use of superposition (in time and space), convolution and deconvolution. The reality of non-linearities are handled on a case by case basis depending on their source (fluid, well or reservoir). Shale gas wells are subject to significant non-linearity over their producing life.
We review some of the fundamental equations that govern pressure and rate transient behavior, introduce several new techniques which are suited to the analysis of data from producing wells and apply them to a synthetic example of a shale gas well.
First, we use simple calculus to show how the convolution integral is derived from standard multi-rate superposition. Then, from the convolution integral, we derive an equation that describes the pressure response due to a step-ramp rate (i.e. an instantaneous rate change from initial conditions followed by a linear variation in rate). It results in a combination of the pressure change due to a constant rate and it's integral. Applying superposition to this equation allows any rate variation to be approximated by a sequence of ramps with far fewer points than those required to achieve the same level of accuracy using standard constant step rate superposition.
Second, we re-write multi-rate superposition functions allowing for stepwise linear variable rate which, when applied to flowing data and used to calculate the pressure derivative, can result in a much smoother response and hence an overall improvement in the analysis of rate and pressure transients recorded from producing wells.
Third, we review the use of the Laplace transform and how it can be applied to discrete data with a view to deconvolving rate transient data.
Finally, we demonstrate how data de-trending can remove the impact of long term non-linearities and apply the methods mentioned above to a synthetic dataset based on a typical shale gas well production profile.
We illustrate the advantages of the newly introduced superposition functions compared to conventional analysis methods when applied to the pressure transients of wells flowing at variable rate.
As an example, we have simulated the production of two shale gas wells over twenty years. Both have the same production profile, but one includes pressure dependent permeability. At various intervals during the life of the well, we introduce a relatively short well test which imposes a small variation in rate but does not include a shut-in. We de-trend the rate transients and then apply the techniques described above to analyse the resulting data. The interpretation allows us to identify non-linearities that may be influencing well productivity over time and to obtain a better understanding of the physics of shale gas production.
The mathematics documented in the paper provides a useful overview of how convolution, superposition, deconvolution and Laplace transforms provide the means to analyse pressure and rate transients for linear systems.
Data de-trending removes the impact of long term non-linearities on shorter transient test periods.
We develop and demonstrate some new and improved techniques for rate and pressure transient analysis, and we illustrate how these can provide insight into the non-linearities affecting shale gas production.
Zhang, Hui (PetroChina) | Wang, Lizhi (Schlumberger) | Wang, Zhimin (PetroChina) | Pan, Yuanwei (Schlumberger) | Wang, Haiying (PetroChina) | Qiu, Kaibin (Schlumberger) | Liu, Xinyu (PetroChina) | Yang, Pin (Schlumberger)
Located at the foothills of Tianshan mountains, western China, the Dibei tight gas reservoir has become one of the key exploration areas in last decade because of its large gas reserve potential. The previous exploration effort yielded mixed results with large variations of the production rates from these exploration wells and many rates are too low to be deemed as discovery wells. Petrophysical properties were excluded as controlling factors because these properties for most exploration wells are very similar. Under the large tectonic stress, heterogeneous natural fracture systems are induced and unevenly distributed in the reservoir, which might be the controlling factor for production. However, due to the limitation of the seismic data quality, quantitative fracture modeling with seismic is not possible for this field. A new method predicting the 3D occurrence of the natural fractures in the reservoir is needed.
In this study, geomechanics-based methods were used to predict the natural fracture systems in the reservoir. The methods started from classification of natural fracture systems based on borehole image and core data into either fold-related and/or fault-related fractures. Geomechanics-based structure restoration was conducted to compute the deformation and the perturbed stress field from the restoration of complex geological structures through time. A correlation was established between the fold-related perturbated stress field and the occurrence of fold-related fractures from wells to predict the 3D occurrence of this type of natural fractures. Meanwhile, the computation of the perturbed stress field around 3D discontinuities (i.e. faults) for one or more tectonic events was conducted by the Boundary Element Method (BEM) until a good match was achieved between the fault-related perturbed stresses and observed fault-related fractures from the wellbore. By using the output from the two methods, the discrete fracture network (DFN) model was constructed to explicitly represent the occurrence and geometry of the natural fracture system in the reservoir in a geological model. A geomechanical model was constructed based on an integrated workflow from 1D to 3D. The fracture stability was then calculated based on the 3D geomechnical model.
Detailed analysis was conducted among the DFN model, the geological model of the reservoir and productivity of the exploration wells, and very good correlation was revealed between the productivity of the exploration wells and the occurrence and geometry of the natural fractures and the structural position of the reservoir.
This study shows that geomechanics-based methods efficiently capture the occurrence of natural fracture systems and reveal the production-controlling factors of the tight gas reservoir. It demonstrates that geomechanics is a powerful tool to support successful exploration of the tight gas reservoir in tectonically stressed environments.
The objective of this study is to visualize the drained rock volume (DRV) and pressure depletion in hydraulically and naturally fractured reservoirs, using a high-resolution simulator to plot streamlines and time-of-flight contours that outline the DRV, based on computationally efficient complex potentials. A recently developed expression based on fast, grid-less Complex Analysis Methods (CAM) is applied to model the flow through discrete natural fractures with variable hydraulic conductivity. The impact of natural fractures on the local development of DRV contours and streamline patterns is analyzed. A sensitivity analysis of various permeability contrasts between natural fractures and the matrix is included. The results show that the DRV near hydraulic fractures is significantly affected by the presence of nearby natural fractures. The DRV location shifts according to the orientations, permeability and the density of the natural fractures. Reservoirs with numerous natural fractures result in highly distorted DRV shapes as compared to reservoirs without any discernable natural fractures. Additionally, the DRV shift due to natural fractures may contribute to enhanced well-interference by flow channeling via the natural fractures, as well as the creation of undrained rock volumes between the natural fractures. Complementary pressure depletion plots for each case show how the local pressure field changes, in a heterogeneous reservoir, due to the presence of natural fractures. The results from this study offer insights on how natural fractures affect the DRV and pressure contour plots. This study uses a fast grid-less and meshless high-resolution flow simulation tool based on CAM to simulate the flow in heterogeneous naturally fractured porous media. The CAM tool provides a practical/efficient simulation platform, complementary to grid-based reservoir simulators.
Data Analytics is progressively gaining traction as a viable resource to improve forecasts and reserve estimations in most prospective US shale plays. Part of those learnings has been tested for the reserves and resources estimation of the next worldwide top-class shale play, Vaca Muerta formation in Argentina. In this work, we rely on advanced artificial intelligence methods to automate workflows for production forecasting and reserve estimation in the Vaca Muerta formation. To achieve this goal, we develop a computational platform capable of integrating several sequential operations into a single automated workflow: (1) data gathering; (2) data preparation; (3) model fitting and forecasting and, (4) EUR estimation. As new data becomes available, each of these steps is performed automatically. The proposed platform also integrates with advanced business intelligence tools that aid at facilitating graphical interpretation and communication among specialists and decision makers. Hence, the suggested workflow can deliver production forecasts several magnitudes faster than traditional workflows while maintaining accurate and engineering sound results. Having fast and reliable forecast turnarounds allow for timely tracking key differences and commonalities among multiple shale plays to facilitate informed decision strategies in unconventional field evaluation and development.