The initial high cost of exploitation of the sustained, increasingly growing development of unconventional resources in Argentina has resulted in concentrating all efforts to increase well productivity while reducing construction and completion costs. The optimization of hydraulic fracture (HF) treatments is vitally important. It is the primary strategy used to achieve an optimal reservoir drainage area, consequently characterizing the fracture geometry, including the height, for the continuous improvement of HF treatment and planning.
Several types of technologies and methodologies are used to estimate fracture height during and after a hydraulic stimulation treatment. These technologies can provide information about the fracture geometry and extension in the near-wellbore (NWB) and far-field areas. The determination of a reliable correlation between those methodologies represents a challenge as a result of formation complexity, heterogeneity, and limitations of evaluation technologies. It is well-known that some areas in the Vaca Muerta formation contain layers that can act as fracture barriers and are responsible for fracture containment.
This paper presents a fast and simple methodology that uses conventional well logs [gamma ray (GR), sonic, and density] from pilot wells to identify potential fracture barriers. This approach establishes a means to evaluate the degree to which the rock will have the ability to control fracture height growth. This methodology was determined useful for planning perforation intervals or clusters placement, particularly in those formations with stress profile showing reduced stress contrast and, when complemented with geological information, this method also provides useful information for horizontal well trajectory. Case studies are provided to illustrate examples of the proposed fracture barrier index (FBI) being calibrated or compared to other fracture height assessment. Additionally, the benefits of adding this new approach to current methodologies and technologies to aid completion design optimization and decision making is discussed.
Hwang, Jongsoo (The University of Texas at Austin) | Sharma, Mukul (The University of Texas at Austin) | Amaning, Kwarteng (Tullow Ghana Limited) | Singh, Arvinder (Tullow Ghana Limited) | Sathyamoorthy, Sekhar (Tullow Ghana Limited)
Understanding injectivity is a critical element to ensure that sufficient volumes of water are being injected into the reservoir to maintain reservoir pressure, to ensure good reservoir sweep and minimize well remediation. It is, however, challenging to describe the large injectivity changes that are sometimes observed in injectors operating under fracturing conditions. This study presents a field case study with the following objectives: 1) explain the complicated injectivity changes caused by fracture opening/closure with injection-rate variations, 2) define a safe operating envelope (for injection pressure and rate) that ensures fracture containment and injection into the target zone, and 3) prescribe how the injection rate should be changed to achieve higher injectivities. Injector operating conditions are developed using results from a full 3-dimensional fracture growth simulation to ensure fracture containment in a multi-layered reservoir.
We present field injectivity observations, a comprehensive simulation workflow and its results to explain injector performance in a deep-water turbidite sand reservoir with multiple splay sands. Understanding the impact on fracture propagation and containment allows us to make quantitative suggestions for the operating envelopes for long-term injection-production management. Strategies for high-rate injection to sustain the injection well performance long-term are discussed.
Simulation results show that, at injection rates over 5,000 bwpd, injection induced fractures propagate. Fracture closure induced by injection shut-down is used to compute the bottom-hole pressure decline as a function of time. The fracture opening/closure events and the thermally induced stress were the primary factors impacting injectivity. The simulation results suggested several ways to improve the injectivity while ensuring fracture containment. Injection under fracturing conditions into a single zone at a high rate is shown to be feasible and this allows us to support a substantial increase in injectivity. This must, however, be done at pressures that will not cause a breach in the bounding shales. The 3-dimensional fracture simulations identified the operating pressure and rate envelope to maximize the injection rates while minimizing the risk of breaching the cap rock and inter-zone shales.
An important aspect of reservoir management process is monitoring and revising plans and an essential component of reservoir management strategy is integration of technologies (Satter et al. 1994). In revisions of reservoir management plans, very rarely do operators incorporate any other data than the data from newly drilled wells and the field production in between the revisions. Any inference in the inter-well space is an interpolation between the well data, as surface seismic is limited by its resolution, as the calibration data is available only at well level. For an effective reservoir management, especially in the decline phase of the field, a logical integration of technologies to capture maximum heterogeneity in the interwell space can be very advantageous. Crosswell technologies, that provide high resolution data between a set of wells, but if used individually, are essentially limited to a very small part of the field having multiple wells. Therefore, in order to monitor and revise reservoir management plans, it is important that such technologies are integrated with full-field solutions. This paper describes a methodology that aims to better manage reservoir by logically integration the 3D reservoir model-a full-field solution-and crosswell electromagnetics/crosswell seismic-an interwell solution.
Application of polymer flooding as a chemical Enhanced Oil Recovery (EOR) has increased over recent years. The main type of polymer used is partially hydrolyzed polyacrylamide (HPAM). This polymer still has some challenges especially with shear stability and injectivity that restrict its utility, particularly for low permeability reservoirs. Injectivity limits the possible gain by acceleration in oil production due to polymer flooding. Hence, good polymer injectivity is a requirement for the success of the operation. This paper aims to investigate the influence of formation permeability on polymer flow in porous media.
In this study, a combination of core flooding with rheological studies is presented to evaluate the influence of permeability on polymer in-situ rheology behavior. The in-situ flow of HPAM polymers has also been studied for different molecular weights. The effect of polymer preconditioning prior to injection was studied through exposing polymer solutions to different extent of mechanical degradation.
Results from this study reveal that the expected shear thinning behavior of HPAM that is observed in rheometer measurements is not observed in in-situ rheology in porous media. Instead, HPAM in porous media exhibits near-Newtonian behavior at low flow rates representative of velocities deep in the reservoir, while exhibiting shear thickening behavior at high flow rates representative of velocities near wellbore region. The pressure build-up associated with shear thickening behavior during polymer injection is significantly higher than pressure differential during water injection. The extent of shear thickening is high during the injection of high Mw polymer regardless of cores' permeability. In low permeable Berea cores, shear thickening and mechanical degradation occur at lower velocities although the degree of shear thickening is lower in Berea to that observed in high permeable Bentheimer cores. This is ascribed to high polymer retention in Berea cores that results in high residual resistance factor (RRF). Results show that preshearing polymer before injection into porous media optimizes its injectability and transportability through porous media. The effect of preshearing becomes favorable for the injection of high Mw polymers into low permeability formation.
This study discusses polymer in-situ rheology and injectivity, which is a key issue in the design of polymer flood projects. The results provide beneficial information on optimizing polymer injectivity, in particular, for low permeability porous media.
Schumi, Bettina (OMV E&P) | Clemens, Torsten (OMV E&P) | Wegner, Jonas (HOT Microfluidics) | Ganzer, Leonhard (Clausthal University of Technology) | Kaiser, Anton (Clariant) | Hincapie, Rafael E. (OMV E&P) | Leitenmüller, Verena (Montan University Leoben)
Chemical Enhanced Oil Recovery leads to substantial incremental costs over waterflooding of oil reservoirs. Reservoirs containing oil with a high Total Acid Number (TAN) could be produced by injection of alkali. Alkali might lead to generation of soaps and emulsify the oil. However, the generated emulsions are not always stable.
Phase experiments are used to determine the initial amount of emulsions generated and their stability if measured over time. Based on the phase experiments, the minimum concentration of alkali can be determined and the concentration of alkali above which no significant increase in formation of initial emulsions is observed.
Micro-model experiments are performed to investigate the effects on pore scale. For injection of alkali into high TAN number oils, mobilization of residual oil after waterflooding is seen. The oil mobilization is due to breaking-up of oil ganglia or movement of elongated ganglia through the porous medium. As the oil is depleting in surface active components, residual oil saturation is left behind either as isolated ganglia or in down-gradient of grains.
Simultaneous injection of alkali and polymers leads to higher incremental oil production in the micro-models owing to larger pressure drops over the oil ganglia and more effective mobilization accordingly.
Core flood tests confirm the micro-model experiments and additional data are derived from these tests. Alkali co-solvent polymer injection leads to the highest incremental oil recovery of the chemical agents which is difficult to differentiate in micro-model experiments. The polymer adsorption is substantially reduced if alkali is injected with polymers compared with polymer injection only. The reason is the effect of the pH on the polymers. As in the micro-models, the incremental oil recovery is also higher for alkali polymer injection than with alkali injection only.
To evaluate the incremental operating costs of the chemical agents, Equivalent Utility Factors (EqUF) are calculated. The EqUF takes the costs of the various chemicals into account. The lowest EqUF and hence lowest chemical incremental OPEX are incurred by injection of Na2CO3, however, the highest incremental recovery factor is seen with alkali co-solvent polymer injection. It should be noted that the incremental oil recovery owing to macroscopic sweep efficiency improvement by polymer needs to be taken into account to assess the efficiency of the chemical agents.
Jia, Ying (Petroleum Exploration and Production Research Institute, SINOPEC) | Shi, Yunqing (Petroleum Exploration and Production Research Institute, SINOPEC) | Huang, Lei (Research Institute of Petroleum Exploration and Development, Petrochina) | Yan, Jin (Petroleum Exploration and Production Research Institute, SINOPEC) | Sun, Lei (SouthWest Petroleum University)
The YKL condensate gas reservoir is one of the biggest condensate gas reservoirs in China and has been developed more than 10years. At present, the combination of subdivision layer, production speed optimization and horizontal well drilling has been the key to economically unlocking the vast reserves of the YKL condensate gas. The primary recovery factor, however, remains rather low due to high capillary trapping and water invasion. While primary depletion could result in low gas recovery, CO2 flooding provides a promising option for increasing the recovery factor.
The objective of this work is to verify and evaluate the effect supercritical CO2 on enhancing gas recovery and analyze the feasibility of CO2 enhance gas recovery (CO2 EGR) of condensate gas reservoir.
Firstly, novel phase behavior experimental procedures and phase equilibrium evaluation methodology for gas-condensate phase system mixed with supercritical CO2 with high temperature were presented. A unique phase behavior phenomena was also reported. Then, CO2 floodingmechanism in condensate gas reservoir was analyzed and clarified based on experiments. Finally, a series of numerical simulation work were conducted as an effective and economical means to maximize natural gas recovery with the lowest CO2 breakthrough by varying strategies, including CO2 injection rate, injection composition, andinjection timing. Meanwhile the CO2 storage volumes of different strategies were calculated.
The results show that higher gas recovery factor can be achieved with CO2 injection through appearing interphase between two fluids, maintaining reservoir pressure, driving gas like "cushion" and controlling water invasion. All strategies have moderate to significant effects on gas production. The control of injection and production ratio needs to be balanced between pressure transient and CO2 breakthrough over the producer to obtain the maximum gas production. The varying injection pressure shows a positive effect of enhancing gas production. Numerical simulation indicated that the recovery of gas reservoir was improved by around 10 percent. The total CO2 storage would be around 30-40% HCPV.
The research showed that CO2 flooding presents a technically promising method for recovering the vast condensate gas while extensively reducing greenhouse gas emissions.
The significant oil reserves related to karst reservoirs in Brazilian pre-salt field adds new frontiers to the development of upscaling procedures to reduce time on numerical simulations. This work aims to represent karst reservoirs in reservoir simulators based on special connections between matrix and karst mediums, both modeled in different grid domains of a single porosity flow model. This representation intends to provide a good relationship between accuracy and simulation time.
The concept follows the Embedded Discrete Fracture Model (EDFM) developed by Moinfar, 2013; however, this work extends the approach for karst reservoirs (Embedded Discrete Karst Model - EDKM) by adding a representative volume through grid blocks to represent karst geometries and porosity. For the extension of EDFM approach in a karst reservoir, we adapt the methodology to four stages: (a) construction of a single porosity model with two grid domains, (b) geomodeling of karst and matrix properties for the corresponding grid domain, (c) application of special connections through the conventional reservoir simulator to represent the transmissibility between matrix and karst medium, (d) calculation of transmissibility between karst and matrix medium.
For a proper validation, we applied the EDKM methodology in a carbonate reservoir with mega-karst structures, which consists of non-well-connected enlarged conduits and above 300 mm of aperture. The reference model was a refined grid with karst features explicitly combined with matrix facies, including coquinas interbedded with mudstones and shales. The grid block of the reference model measures approximately 10 × 10 × 1 meters. For the simulation model, the matrix grid domain has a grid block size of approximately 100 × 100 × 5 meters. The karst grid domain had the same block size as the refined grid. Flow in the individual karst grid domain or matrix grid domain is governed by Darcy's equation, implicitly solved by simulator. However, the transmissibility for the special connections between karst and matrix blocks is calculated as a function of open area to flow, matrix permeability and block center distance. The matrix properties were upscaled through conventional analytical methods. The results show that EDKM had a considerable performance regarding a dynamic matching response with reference model, within a reduced simulation time while maintaining a higher dynamic resolution in the karst grid domain without using an unconstructed grid.
This work aims to contribute to the extension of EDFM approach for karst reservoirs, which can be applied to commercial finite-difference reservoir simulators and it presents itself as a solution to reduce simulation time without disregarding the explicit representation of karst features in structured grids.
The field-scale design of chemical enhanced oil recovery (cEOR) processes requires running complex numerical models that are computationally demanding. This paper provides an efficient screening platform for the cEOR feasibility study by presenting five artificial neural network (ANN) based models. We constructed 1,100 ANN training cases using CMG-STARS to capture the variation in reservoir petrophysical properties and the range of injected chemicals properties for a five-spot pattern. The design parameters were coupled with the reservoir properties using several functional links to optimize the ANN models and improve their performances. The training cases were employed using back-propagation methods to construct one forward model (Model #1) and four inverse models. Model #1 predicts reservoir response (i.e., oil rate, water cut, injector bottomhole pressure, cumulative oil) for known reservoir characteristics (i.e., permeability, thickness, residual oil saturation, chemical adsorption) and project design parameters (i.e., pattern size, chemical slug size and concentration), Model #2 predicts reservoir characteristics by history matching the reservoir response, and Model #3 predicts project design parameters for known reservoir response and characteristics. Models #4 and #5 predict project design parameters for a targeted cumulative oil volume and project duration time, which is useful for economical evaluation before the implementation of cEOR projects.
The validation results show that the developed ANN-based models closely predict the numerical results. In addition, the models are able to reduce the computational time by four orders of magnitude, which is significant considering the complexity of cEOR modeling and the need for reliable and efficient tools in building cEOR feasibility studies. In terms of accuracy, Model #1 has a prediction error of 5% whereas the error for other four inverse ANN models is about 20–40%. To enhance the performance of the inverse ANN models, we changed the ANN structure, increased training cases, and used functional links, which slightly reduced the error. Further, we introduced a back-check loop that uses the predicted parameters from the inverse ANN models as inputs in the forward ANN model. A comparison of back-check results for the reservoir response with the numerical results delivers a relatively small error of 10%, revealing the non-uniqueness of solutions obtained from the inverse ANN models.
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.
The paper provides analytical and semi-analytical solutions to predict the temperature transient behavior of a vertical well producing slightly compressible fluid under specified constant-bottom-hole pressure or rate in a two zone, radial composite no-flow reservoir system, where the inner zone could represent the skin zone, whereas the outer zone represents non-skin zone. The solutions are obtained by solving the decoupled isothermal diffusivity equation for pressure and thermal energy balance equation for temperature for the inner and outer zones by using the finite-difference and Laplace transformation. They be used to simulate temperature transient behavior for the general cases of specified variable bottom-hole or rate production represented by piecewise constants in specified time intervals. The convection, conduction, transient adiabatic expansion and Joule-Thomson heating effects are all considered in solving the temperature equation. Graphical analysis procedures for analyzing such temperature transient data jointly with pressure or rate transient data are also discussed. The results show that sandface temperature first decreases due to adiabatic expansion and then increases due to Joule-Thomson heating for both constant rate and constant bottomhole pressure production cases during infinite-acting flow. During boundary dominated flow, sandface temperature decreases linearly with time due to pore-volume expansion of the fluid over the entire no-flow reservoir system. The time rate of decline is governed by the ratio of the adiabatic-expansion coefficient of the fluid to the volumetric heat capacity of the saturated medium and the pore volume. However, these flow regimes are not well-defined for the constant bottomhole production case because the sandface rate decreases continuously during the infinite-acting radial flow and boundary dominated flow periods and distorts the flow regimes which are well defined on the temperature behavior if the well were produced at a constant rate. Sandface temperature data under specified variable rate or bottom-hole pressure show complicated behaviors and require more general automated history matching methods based on simultaneous use of both sandface temperature and rate transient data sets for parameter estimation.