Ketineni, Sarath Pavan (Chevron Corporation) | Tan, Yunhui (Chevron Corporation) | Hoffman, Katrina L. (Chevron Corporation) | Jones, Matthew (Chevron Corporation) | Ghoraishy, Mojtaba (Chevron Corporation)
Demonstrating the viability of multistage hydraulic fractured horizontal wells to unlock otherwise trapped resources is presented through a case study on Rangely. A combination of high-fidelity reservoir models was employed for accurate forecasts and evaluation of hydraulically fractured horizontal wells to improve resources in this mature conventional oil field with ongoing pressure support and tertiary recovery operations. The modeling techniques used in this method can be extended to other mature oil fields to unlock bypassed oil setting a precedent to re-evaluate mature oil fields with the new unconventional completion technologies.
The Rangely Weber Sand Unit is an Eolian sandstone depositional system consisting of 2 billion bbls of oil in place. The Weber Formation is Pennsylvanian to Permian in age, and typically consists of fine-grained and cross bedded calcareous sandstones. Structurally oil is trapped in an anticline with varying dip angles on the flanks. The oil production from this reservoir was managed through primary depletion for the first two decades of production followed by secondary recovery via water flood and concluding through water alternating CO2 injection (WAG) over the last three decades. Due to the heterogeneity in depositional environment, the recovery factors have been low in the eastern end of the field. The east end of the field has relatively lower permeability and lower porosity compared to the rest of the field. A modeling workflow is presented to assist with evaluation and optimization of hydraulically fractured horizontal infill wells to recover bypassed oil in the eastern end of the Rangely field.
A full fidelity static model was built based on dense, high quality well control data. A sector model was history matched, and then used to update pressure, saturations, and stress distribution to present day. The history matched model was subsequently used to evaluate horizontal well performance and hydraulic fracturing completion options to overcome these heterogeneities and improve recovery from a lower quality reservoir.
Completions optimization opportunities were focused on fracture geometry, incremental Estimated Ultimate Recovery (EUR), and economics. Sensitivity studies demonstrated that an optimal balance of cost and recovery is found at the low end of fracture volumes and wider perforation cluster spacing. Forecasting runs show incremental economic recovery which otherwise could not have been recovered through ongoing WAG operations.
Accurate predictions of connectivity and heterogeneity pose important technical challenges for successful maturation of conventional and unconventional reservoirs. We present the success of a new reservoir management workflow that uses both artificial intelligence and classic models to define the impact of stratigraphic connectivity and heterogeneity on horizontal-well production performance in a mature heavy oil field. The data-driven model based on fuzzy logic was used to compute a new attribute named dynamic Reservoir Quality Index (dRQI). The classical models used the stratigraphic Lorenz Plots, Reservoir Quality Index (RQI) and Flow-Zone indicator (FZI). Workflows were validated through a lookback process on more than 400 wells used to predict the fine-scale stratigraphic and directional heterogeneities within intervals targeted by horizontal wells, and production performance. The workflow was successfully used to optimize the horizontal well placement for 2019-2020 drilling programs.
Jackson, A. C. (Chevron Corporation) | Dean, R. M. (Chevron Corporation) | Lyon, J. (Chevron Corporation) | Dwarakanath, V. (Chevron Corporation) | Alexis, D. (Chevron Corporation) | Poulsen, A. (Chevron Corporation) | Espinosa, D. (Chevron Corporation)
Reservoir management for an economically successful chemical EOR project involves maintaining high injectivity to improve processing rates. In the Captain Field, horizontal injection wells offshore have been stimulated with surfactant-polymer fluids to reduce surrounding oil saturations and boost water relative permeability. The surfactant-polymer stimulation process described herein enables a step change in injectivity and advances the commercialization of this application. This paper explains the damage mechanism, laboratory chemical design, quality control through offshore field execution and data quantifying the results.
Phase behaviour laboratory experiments and analytical injectivity models are used to design a near wellbore clean-up and relative permeability improvement. Three field trials were conducted in wells that had observed significant injectivity decline over 1-3 years of polymer injection. Surfactant and polymer are blended with injection water and fluid quality is confirmed at the wellheads. Pressure is continuously monitored with injectivity index to determine the chemical efficiency and treatment longevity. Oil saturation changes and outflow profile distributions are analysed from well logs run before and after stimulating. Learnings are applied to refine the process for future well treatments.
The key execution elements include using polymer to provide adequate mobility control at high relative permeability and ensure contact along the entire wellbore. Repeatability of success with surfactant-polymer injection is demonstrated with decreased skin in all the wells. The key results include the oil saturation logs that prove the reduction of oil near the well completion and improves the relative permeability to aqueous phase. The results also prove to be sustainable over months of post-stimulation operation data with high injectivity.
Injectivity enhancement was supported by chemical quality control through the whole process. From laboratory to the field (from core flood experiments to dissolution of trapped oil near wellbore), surveillance measurements prove that the chemical design was maintained and executed successfully. The enhanced injectivity during clean-up allows for higher processing rate during polymer injection and negates the need for additional wells.
The application of surfactant-polymer technology can rejuvenate existing wells and avoid high costs associated with redrilling offshore wells. This improves processing rate for EOR methods and can even be applied to waterflood wells to improve the injectivity, e.g low permeability reservoirs.
Vaca Muerta shale is among the most promising unconventional plays outside of North America. Like other shale plays, it is developed using multistage hydraulic fracturing technology. In this experiment, dual microseismic arrays were deployed during stimulation to monitor fracture geometry. Beyond location of events, moment tensor inversions were performed on three horizontal wells. Fault plane solutions were derived based on the double-couple model from moment tensor results. Then the fault plane solutions were used as inputs for calculating stress field by minimizing the misfit between regional stress field and a cluster of fault plane solutions. The stress inversion results show that the stress direction derived from each stage is consistent with regional tectonic stress direction. The post-stimulation stress regimes vary between strike-slip and reverse faulting, which indicates stress shadowing effects among neighbor stages. The uncertainties are estimated using the statistical bootstrapping method. In addition, the stress inversion results were compared with ISIP (Instantaneous Shut-In Pressure) data and b-values and show some level of consistency.
Vaca Muerta Shale in the Neuquen Basin of central west Argentina is a massive unconventional resource play outside of North America. The same technology of horizontal drilling combined with multi-stage hydraulic fracturing are being implemented to extract hydrocarbon from the tight organic rich shale formation. The quality of rock is comparable to the major plays in North America such as Marcellus, Eagle Ford and Bakken (Cataldo et al. 2016). Argentina is estimated to have the world's second largest shale gas reserve according to US Energy Information Administration. Production from Vaca Muerta is rapidly increasing, with a prediction up to 1 million BOE/D in 15 years (Donnelly 2018).
Microseismic has evolved into a mainstream monitoring technique for hydraulic fracturing. The advantage from microseismic is the 4D coverage in space and time. However, the majority of microseismic monitoring projects only uses the location information to define stimulated rock volume. Going beyond “dots in the box” becomes an urgent need for the microseismic community to provide better information to influence the operational design (Tan et al. 2014; Zhang et al. 2018).
Zhang, Yanbin (Chevron Corporation) | Yang, Changdong (Chevron Corporation) | He, Jincong (Chevron Corporation) | Wang, Zhenzhen (Chevron Corporation) | Xie, Jiang (Chevron Corporation) | Wen, Xian-Huan (Chevron Corporation)
Many shale and tight reservoirs produce significant amount of water. The produced water may come from the injected water pumped during hydraulic fracturing, the formation water, or both. The injected water occupies the fracture network and likely imbibes into the formation close to the fractures. It contributes primarily to early water production during the flowback period. The formation water, when above the critical water saturation, will contribute to most of the long-term water production. Traditional production analysis methods such as DCA and RTA will not be able to characterize the water saturation profile from fracture to formation, nor the interaction between water and hydrocarbon flow in the formation. As a result, water production data is either totally ignored or lumped together with hydrocarbons, leading to inaccurate estimation of reservoir parameters and unreliable forecast of hydrocarbon and water production streams.
Limitations of these traditional production data analysis methods also apply to oil wells that exhibit rapid increases in GOR, as well as gas condensate wells dropping below the dew point. In these cases, the phase behavior of reservoir fluid becomes extremely important, particularly near the hydraulic fractures where the pressure gradient is the highest. 3D full-physics reservoir simulation has been used in the past in these situations, but has faced challenges such as model complexity, water initialization, long simulation time, and difficulty to history match.
We recently proposed a model-based data-driven approach using the Diffusive Diagnostic Function (DDF). In this paper, we will show that the DDF approach can address the above-mentioned difficulties when dealing with multiphase production data in shale and tight reservoirs. It simplifies the 3D problem into 1D simulation models, so that a single forward simulation takes seconds while is still able to capture complex phase behavior and multiphase flow near the fracture and in the formation. It can efficiently and automatically history match multiphase production data using the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) algorithm.
Wang, Shugang (Chevron Corporation) | Tan, Yunhui (Chevron Corporation) | Sangnimnuan, Anusarn (Chevron Corporation) | Khan, Shahzad (Chevron Corporation) | Liang, Baosheng (Chevron Corporation) | Rijken, Peggy (Chevron Corporation)
Selecting the best tool for a specific type of reservoir condition is a crucial part of a fluid sampling job. Moreover, uncertainty in sample quality increases when the fluid phases are miscible. On a recent logging job, a formation tester was used to acquire water samples across a zone drilled with water-base mud (WBM). We examine the performance of several probe configurations (both existing and prototype) under equivalent reservoir conditions to quantify and optimize filtrate cleanup efficiency. The study is carried out using a compositional simulator for a water-saturated reservoir invaded with blue-dye tracer included in WBM filtrate.
History matching of field measurements allows the calibration of the model for further modification to account for a variety of reservoir conditions. Complex tracer dynamics of a blue-dye WBM invading a water-saturated formation and fluid pumpout are accurately and expediently modeled using a flexible numerical algorithm to account for different probe types and tool configurations. Under normal operating conditions, the chosen formation tester would have taken around one hour to clean the filtrate contamination to a target value of 5%. On the other hand, the best choice was the Focused Elliptical Probe, for which fluid cleanup would take less than 40 minutes. Additionally, a different tool configuration with a combination of multiple probe geometries spaced radially around the tool would provide faster cleanup times of only 32 minutes, thereby saving rig time.
We rank eight formation testing tools designs under equivalent reservoir conditions. The examples highlight the importance of probe geometry and configurations together with reliable and expedient numerical modeling during the pre-job phase to reduce cleanup time in anticipation of complex reservoir conditions. Furthermore, numerical simulations compare the fluid cleanup efficiency for various commercial formation-testing probes together with innovative probe designs that could potentially lead to a new tool or probe development. Perfecting both probe geometry and fluid pumping schedule is the most important output of our study.
Kumar, Sarwesh (Chevron Corporation) | Talpallikar, Milind (Chevron Corporation) | Valbuena, Ernesto (Chevron Corporation) | Nguyen, Phan (Chevron Corporation) | Velusamy, Baskar (Chevron Corporation)
Increased complexity in reservoir models, advanced understanding of fluid flow physics, and improved computational capabilities continuously push E&P companies to improve their reservoir simulation tools and associated workflows. This paper describes a robust framework to handle two critical steps in the development of the integrated simulation workflows: simulator testing and release process to end-users companywide. Selection of benchmark models, coverage of functionalities and stress testing of the simulator, are also discussed.
Ideally, the simulation results should not change with new versions of the simulation software, but this is hardly the case. With the addition of new features, bug fixes and updates, some of the existing functionalities could behave differently - sometimes an improvement over previous results and at other times, an unintentional side-effect leading to errors. The simulator testing process starts with the automated regression test covering a wide range of features and functionalities, which is evaluated through comprehensive benchmark criteria and metrics for success/failure. Regression tests are followed by different levels of investigation to identify and resolve potential problems within each individual software component, the integrated workflows, or the simulation model itself. The web-based continuous integration tool (to collect code changes and create new builds) along with the automation helped to reduce the turnaround time for identification of issues with any code changes. This also helped to streamline and prioritize the scheduling and investigation of the regression tests based on the criticality of features under development and perform focused testing for user-preferred workflows.
Due to the increase in operational requirements, reservoir complexities, and the need to share the models with the partners, several types of simulation workflows are in practice within the company, all of which require proper testing before each new release version. Previously, sheer volume and monotonicity of the regression test process caused frequent incidences of human error; therefore, automation has not only reduced those errors but also made quality time available for in-depth analysis of the issues shortlisted by the automated process.
The paper details the testing and release framework using several real field simulation models, where the automated process resulted in timely resolution of issues, seamless transition to newer version of software/workflows, and reliable results from simulation tools. This framework also facilitated the collaboration and coordination among the cross-functional teams and provided updates on the release status and software changes to ensure a smooth and successful release.
This paper presents novel approaches and comprehensive field case examples on applying water chemistry in reservoir management and production. Systematic field water sampling and analysis, data integration, and water chemistry fingerprinting techniques are utilized for various important applications such as Original Oil In Place (OOIP) estimate, water source identification, prediction/prevention/management of oilfield scale and other water-related production/operation problems. Field case study examples show significant value creation achieved by utilizing water chemistry-based approaches. Results show subsurface water heterogeneity can significantly impact the calculation of OOIP, water sampling and analysis is critical to identify "unexpected" scaling risk at initial water breakthrough and monitor seawater breakthrough ensuring management/treatment in place as needed, systematic water data collection and integration and understanding can be used as a reliable/efficient/cost-effective approach to identify water source/water breakthrough from a new formation zone. Significant value creation was achieved for projects via our novel and systematic water chemistry-based approach discussed in this paper.
The use of full-physics models in close-loop reservoir management can be computationally prohibitive as a large number of simulation runs are required for history matching and optimization. In this paper we propose the use of a physics-based data-driven model to accelerate reservoir management and we describe how it could be implemented with a commercial simulator.
In the proposed model, the reservoir is modeled as a network of 1D flow paths connecting perforations at different wells. These flow paths are discretized and the properties at each grid block along each flow path are derived from history matching of production data. To simulate flow in this network model through a commercial simulator with all the physics, an equivalent 2D Cartesian model is set up in which each row corresponds to one of the 1D flow paths. Finally, the history matching is performed with ensemble smoother with multiple data assimilation (ESMDA).
The proposed network model is tested on both waterflood and steamflood problems. It is demonstrated that the proposed model matches with well-level production history (including pressure and phase flow rate) well. The calibrated ensemble from ESMDA also provided a satisfactory probabilistic forecast of future production that almost always envelops the true solutions. This indicates that the proposed model, after calibrated with production data, is accurate enough for production forecast and optimization. In addition, the use of commercial simulator in the network model provided flexibility to account for complex physics, as demonstrated by the successfully application to the steamflood problem. Compared with traditional workflow that goes through the full cycle of geological modelling, history matching and probabilistic forecasting, the proposed network model only requires production data and can be built within hours. The resulted network model also runs much faster than a full-physics as it typically has much less grid blocks. We expect the proposed method to be most useful for mature fields when abundant of production data is available.
As far as we know, this is first time a physics-based data-driven model is implemented with a commercial simulator. The use of commercial simulator makes it easy to extend the model for complex reservoir such as thermal or compositional reservoirs.