Results
Abstract In this paper will describe the integrated workflow between two well-known reservoir engineering approaches PTA and 3D dynamic simulation. This workflow provides significant help to understanding the channel sand reservoir properties, predict possible well interference and provide consistent well's production prediction forecast. The available geological background, seismic data to map the combined channels, and even well by well Petrophysical correlation showed limited capabilities to characterize the channel heterogeneity due to the reservoir continuity change consistently, and for most of the cases the channels features are unpredicted. Giving these limitations, it is necessary to consider using other engineering approach; e.g. Pressure Transient Analysis (PTA), Rate Transient Analysis (RTA), Dynamic Simulation models to narrow down the uncertainties for better understanding of the reservoir quality. This customized workflow was completed and used in this study to cover all uncertainty parameters during the calibration and prediction of the wells’ performance. It consists to calibrate the geological model using Pressure transient Analysis (PTA) and dynamic simulation model output to enhance the geological model calibration assessment under uncertainty. As results; multiple geological realizations were obtained to cover the possible uncertainty ranges for each parameter. Furthermore, the Well prediction scenarios were performed to evaluate the range of wells’ production prediction forecast under multiple geological realization uncertainty to be more confident in predicting the wells performance under different well constrains and come up with minimum and maximum values. The main challenge associated with the development of channel tight gas sand reservoirs is how to predict the channel attributes and location; e.g. channel width, height, and how to quantify their effect on the well's long-term performance. This workflow will help to quantify the channel properties, interference between wells, and it will guide you on how to select the new well locations for better business planning.
- North America > United States > Texas (0.69)
- Asia > Middle East > Saudi Arabia (0.47)
- North America > United States > Louisiana (0.46)
- North America > United States > Colorado (0.46)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Pressure transient analysis (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Drillstem/well testing (1.00)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning (0.48)
Simultaneous Auto-Calibrations of Complex Fracture Configurations in Multi-Well Development Scenario in Uinta Basin with Embedded Discrete Fracture Model and Multivariate Gaussian Distributions
Xiao, Yuchen (The University of Texas at Austin) | Liu, Chuxi (The University of Texas at Austin) | Yu, Wei (SimTech LLC) | Sepehrnoori, Kamy (The University of Texas at Austin) | Zigler, Corwin (The University of Texas at Austin)
Abstract In unconventional reservoirs, several horizontal wells are drilled within each well pad, and could have different production profiles. Automatic history matching (AHM) is the process of inversely calibrating fracture geometry based on field production. The main challenge of AHM is to reduce the uncertainty of model parameters for a single well. This challenge is amplified for multi-well history matching, and limited research has been dedicated to AHM of multiple wells. This work presents a systematic workflow to be the first capable to characterize fracture configurations and other properties of a well-pad with only 6 hours of computational cost for 2 wells' calibration. We initialize a mean vector and a variance-covariance matrix with zeros on the off-diagonals of model parameters for each well and sample 50 sets of uncertain parameters from the initialized Multivariate Gaussian distributions (MGD). Each sample of model parameters is fed into the embedded discrete fracture model (EDFM) along with a reservoir simulator to obtain the modeling result. A multi-objective loss function is used to compute the global error where 10 best modeling results with minimum global errors are selected, and these 10 sets of model parameters are used to update the mean vector and the variance-covariance matrix of the MGD. In the second iteration, the model parameters are sampled from the updated MGD, and the process repeats until the modeling results converge to history value based on a user-defined error threshold. We applied this novel workflow to two shale oil horizontal wells in Uinta Basin. The results show exceptional match between the best simulation model and the field production observation. The final variance-covariance matrix shows significantly reduced uncertainty in all model parameters compared to the initial variance-covariance matrix. The variance-covariance matrix also captures the inter-correlations between model parameters where the inter-correlations act as a sampling constraint which eliminates non-physical samples and significantly improves sampling efficiency. The proposed workflow's performance is robust against poor initial parametrizations. This is because the mean vector is updated through each iteration and always shifts it towards the optimal combination of model parameters, even when the initial iteration samples are sub-optimal. The proposed workflow has improved the matching between modeling results and the field observations at only a quarter of computational cost compared to other AHM workflows. Results show that our workflow obtains better global optimums of model parameters with high precision and allows us to provide superior characterizations of fracture properties/geometries of multi-wells in a well pad setting, providing valuable suggestions for well spacing optimizations.
- North America > United States > Colorado (0.85)
- North America > United States > Wyoming (0.71)
- North America > United States > Utah (0.61)
- North America > United States > Texas > Harris County > Houston (0.15)
- North America > United States > Wyoming > Uinta Basin (0.99)
- North America > United States > Wyoming > Laramie Basin > Niobrara Formation (0.99)
- North America > United States > Utah > Uinta Basin (0.99)
- (5 more...)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.34)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.31)
Abstract Well spacing in unconventional fields is a challenging task because the hydraulic fracture geometry is not fully known and there is uncertainty about reservoir properties. Integrating geomechanics medeling with reservoir simulation is a cost-effective and efficient approach for generating realistic fracture geometry, estimating fracture conductivity, and understanding fluid flow behavior. This paper presents a case study of integrated geomechanical and reservoir flow simulation validated with fracture and reservoir diagnostics for the purpose of determining ideal well spacing. In a Rocky Mountain Powder River Basin field, a standalone horizontal well in the Turner reservoir was produced for almost a year before drilling adjacent infill producers. FMI logs recorded in the infill wells indicated hundreds of hydraulic fractures growing from the parent well. Based on the distances and the hydraulic fracture density from the image logs, a 3D fracture model was calibrated for the parent well. The fracture geometry and simulated proppant concentration were then exported to a reservoir simulator, where production was history matched. This simulation was constrained by measured far-field pressure data from the DFIT in each wellbore. A rigorous fracture conductivity calculation workflow was developed and applied before reservoir simulation. The conductivity workflow uses simulated proppant concentration from the fracture model and conductivity measurements of propped and unpropped fractures to define a conductivity distribution for reservoir simulation. Additionally, a proppant creep function was developed to characterize proppant-pack conductivity degradation over time. By applying the fracture conductivity calculation workflow and the measured far-field pressure constraints, the production history was matched for the full parent wellbore. The simulation results show that the parent well's drainage are covers some of the infill well's targeted reservoir. Subsequent hydraulic fracture simulations of multiple infill wells then demonstrated the effect of parent well depletion and rock stresses on the hydraulic fracture geometry of the infill wells. Our case study illustrates: (a) how modeled hydraulic fracture geometry can be calibrated using image logs recorded in the laterals of infill wells, (b) how a new proppant conductivity algorithm can be used to assign propped and unpropped fracture conductivity based on physics-based models of proppant concentration, (c) how reservoir simulation can be constrained with a calibrated fracture model and far-field pressure measurements, and (d) how a new proppant creep function can be used to describe proppant-pack conductivity degradation over time.
- North America > United States > Wyoming (0.85)
- North America > United States > Texas (0.68)
- North America > United States > Montana (0.61)
- Research Report > New Finding (0.48)
- Research Report > Experimental Study (0.48)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.47)
- Geology > Structural Geology > Tectonics > Compressional Tectonics > Fold and Thrust Belt (0.34)
- North America > United States > Wyoming > Powder River Basin (0.99)
- North America > United States > Montana > Powder River Basin (0.99)
- North America > United States > Wyoming > Laramie Basin > Niobrara Formation (0.94)
- (4 more...)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning (0.34)
A Probabilistic Well Spacing Optimization Workflow for Shale Gas Reservoirs With Natural Fractures
Sun, Weitong (China National Oil and Gas Exploration and Development Co., Ltd.) | Li, Qiwei (The University of Texas at Austin) | Liu, Chuxi (The University of Texas at Austin) | Yu, Wei (Sim Tech LLC) | Gong, Yiwen (Sim Tech LLC) | Sepehrnoori, Kamy (The University of Texas at Austin)
ABSTRACT: Producing from shale formations has been made profitable because of technological advancements of hydraulic fracturing and horizontal drilling. However, the complexity and uncertainties of the shale reservoirs make it hard to estimate the assets and maximize the value by optimizing the multi-well placement. Reservoir simulation is a powerful tool to estimate the performance of reservoir but calibrating models and optimizing the well spacing can take lots of human efforts and computation time, especially when we need multiple models to stress the uncertainties. Thus, an efficient way to improve the efficiency of simulations and reduce the number of simulations needed can be helpful for the decision-making. In this study, a probabilistic well spacing optimization workflow was developed that can help engineers understand uncertainties and ease decision making process. The EDFM (embedded discrete fracture model) method was applied to efficiently model hydraulic and natural fractures in shale reservoirs. The different combinations of uncertain reservoir and fracture parameters were effectively captured through performing assisted history matching. We present an example of well spacing optimization in a shale gas reservoir with complex natural fractures. 1. Introduction The optimal well spacing is crucial for the economic development of unconventional reservoirs. Disproper well spacing will reduce oil and gas production, as well as economic benefits (Yu and Sepehrnoori, 2018). Therefore, it is essential to find an optimal well spacing that can balance Estimated Ultimate Recovery (EUR) and economic evaluation for hydraulically fractured horizontal wells in shale reservoirs. Several studies have focused on well spacing optimization numerically and analytically. However, because of the shale reservoirs' complexity and uncertainties, it is challenging to accurately calibrate the subsurface uncertainties associated with hydraulic and natural fractures (Chang et al., 2020). Assisted history matching is a good method to capture the realization of uncertainties (Li et al., 2020). Another challenge is how to appropriately model the complex fractures network, which plays a critical role in gas recovery (Yu et al., 2018). The actual fracture geometry is complicated, especially when natural fractures exist (Sepehrnoori et al., 2020). Embedded Discrete Fracture Model (EDFM) was proposed to overcome this issue. It can save more than 90% time than the Local Grid Refinement (LGR) method while maintaining accuracy (Xu et al., 2017a, 2017b, 2018). Therefore, EDFM is the best approach to accurately and efficiently establish natural fractures and hydraulic fractures in gas reservoirs with high flexibility.
- North America > United States > Texas > Travis County > Austin (0.16)
- North America > United States > Texas > Harris County > Houston (0.15)
- Information Technology > Artificial Intelligence > Machine Learning (0.94)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.63)
Abstract Leveraging publicly available data is a crucial stepfor decision making around investing in the development of any new unconventional asset.Published reports of production performance along with accurate petrophysical and geological characterization of the areashelp operators to evaluate the economics and risk profiles of the new opportunities. A data-driven workflow can facilitate this process and make it less biased by enabling the agnostic analysis of the data as the first step. In this work, several machine learning algorithms are briefly explained and compared in terms of their application in the development of a production evaluation tool for a targetreservoir. Random forest, selected after evaluating several models, is deployed as a predictive model thatincorporates geological characterization and petrophysical data along with production metricsinto the production performance assessment workflow. Considering the influence of the completion design parameters on the well production performance, this workflow also facilitates evaluation of several completion strategies toimprove decision making around the best-performing completion size. Data used in this study include petrophysical parameters collected from publicly available core data, completion and production metrics, and the geological characteristics of theNiobrara formation in the Powder River Basin. Historical periodic production data are used as indicators of the productivity in a certain area in the data-driven model. This model, after training and evaluation, is deployed to predict the productivity of non-producing regions within the area of interest to help with selecting the most prolific sections for drilling the future wells. Tornado plots are provided to demonstrate the key performance driversin each focused area. A supervised fuzzy clustering model is also utilized to automate the rock quality analyses for identifying the "sweet spots" in a reservoir. The output of this model is a sweet-spot map that is generated through evaluating multiple reservoir rock properties spatially. This map assists with combining all different reservoir rock properties into a single exhibition that indicates the average "reservoir quality"of the formation in different areas. Niobrara shale is used as a case study in this work to demonstrate how the proposed workflow is applied on a selected reservoir formation whit enough historical production data available.
- North America > United States > Wyoming (1.00)
- North America > United States > Texas (1.00)
- North America > United States > Colorado (0.89)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.72)
- Geology > Geological Subdiscipline > Geomechanics (0.54)
- North America > United States > Wyoming > Laramie Basin > Niobrara Formation (0.99)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.99)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Eagle Ford Shale Formation (0.99)
- (31 more...)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Data mining (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.96)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Clustering (0.69)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Fuzzy Logic (0.69)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Regression (0.48)
Understanding the Multiphysical Processes in Carbon Dioxide Enhanced-Oil-Recovery Operations: A Numerical Study Using a General Simulation Framework
Wang, Shihao (Colorado School of Mines) | Di, Yuan (Peking University) | Winterfeld, Philip H. (Colorado School of Mines) | Li, Jun (King Fahd University of Petroleum and Minerals) | Zhou, Xianmin (King Fahd University of Petroleum and Minerals) | Wu, Yu-Shu (Colorado School of Mines) | Yao, Bowen (Colorado School of Mines)
Summary In this paper, we aim to enhance our understanding of the multiphysical processes in carbon dioxide (CO2)-enhanced-oil-recovery (EOR) (CO2-EOR) operations using a modeling approach. We present the development of a comprehensive mathematical model for thermal/hydraulic/mechanical (THM) simulation of CO2-EOR processes. We adopt the integrated-finite-difference method to simulate coupled THM processes during CO2-EOR in conventional and unconventional reservoirs. In our method, the governing equations of the multiphysical THM processes are solved fully coupled on the same unstructured grid. To rigorously simulate the phase behavior of a three-phase, nonisothermal system, a three-phase flash-calculation module, dependent on the minimization of Gibbs energy, is implemented in the simulator. The simulator is thus applicable to both miscible and immiscible flooding simulations under isothermal and nonisothermal conditions. We have investigated the effect of cold-CO2 injection on injectivity as well as on phase behavior. We conclude that cold-CO2 injection is an effective way to increase injectivity in tight oil reservoirs and reduces overriding effect in high-water-bearing reservoirs. Using the developed general simulation framework, we have discovered and studied several intriguing multiphysical phenomena that cannot be captured by commonly used reservoir simulators, including the temperature-decreasing phenomenon near the production well and the permeability-enhancement effect induced by the thermal unloading process. These phenomena can be captured only by the fully coupled multiphysical model. The novelty of this paper lies in its integration of multiple physical simulation modules to form a general simulation framework to capture realistic flow and transport processes during CO2 flooding, and in revealing the behavior of cold-CO2 injection under THM effects.
- North America > United States > Texas (1.00)
- Europe (0.67)
- North America > United States > Wyoming > Laramie Basin > Niobrara Formation (0.99)
- North America > United States > Nebraska > Laramie Basin > Niobrara Formation (0.99)
- North America > United States > Kansas > Laramie Basin > Niobrara Formation (0.99)
- (4 more...)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.68)
Abstract One of the challenges of unconventional resource development is the identifying and preventing casing failures caused by the hydraulic fracturing process. Multiple mechanisms may be responsible for casing deformation and/or failures, starting with the rock properties of the formation, the wellbore configuration, quality control of tubulars, and operational aspects during drilling and completion. This paper presents two case studies where casing issues were discovered during the drill out of frac plugs following multi-stage fracturing treatments. The objectives of these studies are (a) to determine the cause and nature of the casing failures, (b) to recommend changes to future completion programs to prevent similar operational issues, and (c) to develop a model that automatically identifies these failures. The subject wells are located in two very different basins: the Eagle Ford trend in the Brazos Valley (BV) area of south Texas and the Powder River Basin (PRB) in Wyoming. In both studies, the casing issues could be directly correlated to Abnormal Pressure Behaviors (APBs) observed during fracturing. A total of 486 stages, completed in 12 different wells, were reviewed using a cloud-based application that allows stages to be examined individually, or as groups. Since then, five additional wells have been added to the data set. After problem stages were identified, the completion team worked with the drilling engineers and geologists to determine the mechanisms causing the casing damage. Tight spots encountered during frac plug drill out in the BV wells directly correlated with stages completed in geological transition zones between the Eagle Ford and Woodbine formations. Once this was recognized, the team implemented operational contingencies to fracture designs for stages completed in BV transition zones. In the PRB wells, after reevaluating the post-mill inspection of the casing, the damage was found to be poor casing quality control. The location of casing deformations and/or failures directly correlated with stages that displayed evidence of frac plug failure. Moving forward, the PRB completion supervisors were made aware of potential issues, and alternative procedures were developed for both fracturing and drill out operations that utilized the questionable casing. As of this time, no additional casing issues have occurred. In these studies, identification of the problem stages was initially performed manually (stage-by-stage) using a cloud-based analytics platform (CBAP). During the process, it was recognized that the two types of problem stages had their own characteristic pressure signature. A machine learning algorithm was developed that automatically identifies plug failure, which is indicated by a sudden unexplained pressure drop in the absence of rate changes. Transition stages could be easily identified through the use of stage variance plots (e.g., comparing maximum/average rates and pressures across multiple stages and wells) and also through machine learning algorithms that identified unexpected pressure increases followed by sharp pressure drops.
- Information Technology > Artificial Intelligence > Machine Learning (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Diagnosis (0.35)
Hydraulic fracturing of lateral shale wells generates complex fracture networks with predominantly vertical fracture sets. Evaluating stimulated reservoir volume (SRV) is critical to understanding well productivity. To this end, we have developed a quantitative interpretation to evaluate fracture height and density from time-lapse DAS VSP data. We model P-wave time delays due to induced vertical fracture sets as horizontal transverse isotropic (HTI) zones, and we use rock physics to relate seismic anisotropy to fracture properties. An inversion of P-wave time delays can then recover the fracture heights and densities for each stage of the stimulation. DAS VSP field data from two horizontal wells were acquired and analyzed, with fracture heights and densities consistent with independent fracture diagnostics, such as microseismic. In principle, analysis relies only on P-wave time delays, so it could be applied in real time during stimulation operations. Presentation Date: Wednesday, October 14, 2020 Session Start Time: 1:50 PM Presentation Time: 1:50 PM Location: 361F Presentation Type: Oral
- North America > United States > Wyoming (0.29)
- North America > United States > Colorado (0.29)
- North America > United States > Wyoming > Laramie Basin > Niobrara Formation (0.99)
- North America > United States > Wyoming > DJ (Denver-Julesburg) Basin > Codell Formation (0.99)
- North America > United States > Texas > Permian Basin > Midland Basin (0.99)
- (5 more...)
Abstract There is a very extensive amount of information and learnings from naturally fractured reservoirs (NFRs) around the world collected throughout several decades. This paper demonstrates how the information and learnings can be linked with tight and shale reservoirs (TSRs) with the objective of maximizing hydrocarbon recovery from TSRs. A classic definition indicates that a natural fracture is a macroscopic planar discontinuity that results from stresses that exceed the rupture strength of the rock (Stearns, 1982). Stearns' definition has been applied successfully for several decades. In this paper, the definition is extended to include not only macroscopic planar discontinuities but also planar and sinuous discontinuities that extend throughout different scales including micro and nano fractures. The paper demonstrates that, as in the case of the continuum that exists in process speed (the ratio of permeability and porosity, Aguilera, 2014), there is also a continuum of pore throat apertures of different sizes, natural fractures with different apertures, and Biot coefficients for different rocks. All of these directly or indirectly which affect reservoir performance. Actual observations in TSRs indicate that micro and nano natural fractures do not flow significant volumes of oil or gas toward horizontal wells. Thus, the wells must be hydraulically fractured in multiple stages to achieve commercial production. Once the wells are hydraulically fractured, the area exposed to the shale reservoir is enlarged and the natural micro and nano fractures flow hydrocarbons toward the hydraulic fracture, which in turn based on the values of hydraulic fracture permeability, feeds those hydrocarbons to the wellbore. In TSRs there are also completely cemented macroscopic fractures that are breakable by hydraulic fracturing and can become very effective conduits of hydrocarbons toward the wellbore. The link that exists between natural fractures at significantly different scales established in this paper is a valuable observation. This is so because the larger tectonic, regional and contractional (diagenetic) fractures that exist in NFRs have been studied extensively for several decades, for example in carbonates, sandstones, and basement rocks. Those learnings from NFRs have not been used to full potential in TSRs for maximizing oil and gas recoveries. This paper provides the necessary tools for remediating that situation. The established link between NFRs and TSRs permits determining how to drill and complete wells in TSRs. It is concluded that this link will lead to (1) improvements in gas production performance, and (2) maximizing economic oil rates and recoveries under primary, improved oil recovery (IOR) and enhanced oil recovery (EOR) production schemes.
- North America > United States > Texas (1.00)
- North America > United States > Oklahoma (1.00)
- North America > United States > Kansas (1.00)
- (9 more...)
- Research Report (0.45)
- Overview (0.45)
- Phanerozoic > Mesozoic (0.68)
- Phanerozoic > Paleozoic > Devonian (0.45)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Petroleum Play Type > Unconventional Play > Shale Play (1.00)
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (1.00)
- Geophysics > Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > North America Government > United States Government (0.46)
- South America > Colombia > Middle Magdalena Basin > La Luna Shale Formation (0.99)
- South America > Argentina > Patagonia > Neuquén > Neuquen Basin > Vaca Muerta Shale Formation (0.99)
- North America > United States > Wyoming > Sand Wash Basin (0.99)
- (111 more...)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.46)
Abstract Determination of well spacing and pad sequence are critical factors for maximizing production and yet remain challenging in unconventional plays. Data analysis of existing well performance provides an extensive knowledge base of ever changing designs, impact on performance and locations, however, integrated modeling has gained more attention despite the increase in time and effort. The scope of this paper is to illustrate a practical workflow alternative for rigorous multiple uncertainty analysis and optimization study that have been successfully applied to our unconventional reservoir factory-model development in the Permian Basin. Considering scenarios of multiple wells and pads with different sequences of completions and production, the workflow consists of three key phases. First, complex hydraulic fractures with a discrete naturally fractured network is modeled and converted to an unstructured reservoir simulation grid fitting simulator. The simulated well production performances then serve as a reference case. Second, the hydraulic fractures are further characterized by a logarithmic local grid refinement approach through an in-house reservoir simulation package designed for creating multiple realizations for history matching, uncertainty assessment, and optimization study, given reservoir heterogeneity and fracture variability. The whole section with different well spacing and pad sequence is then evaluated. Third, an in-house uncertainty analysis package is linked to both hydraulic fracture modeling and the in-house reservoir simulation platform for a variety of parameters including pad sequencing strategy/timing, well spacing, economic limit, matrix permeability, fluid type, and drawdown pressure. This workflow is fast and systematic while capturing the fracture geometry from complex hydraulic fracture modeling. Results from recent Midland Basin evaluations demonstrated that well interference should be considered at section level with all the wells to ensure proper section EUR consideration and different scenarios of pad sequences noticeably affect the section EUR, depending on the time differences, matrix permeability, fluid type and drawdown management. The novelty of this workflow is that 100s of realizations of different scenarios are created with the run time that is much faster yet results very similar to the more complex model’s result. Characteristics of Strategic Decisions in the Unconventional Strategic decisions for unconventional reservoir development have the following characteristics and are quite different with those in conventional reservoirs: Well Prioritization. There are many options to drill, complete and produce wells in an unconventional asset, given many sections with big areas and multiple vertical targets. The development sequence is laid out through overall economic prioritization.
- South America > Argentina > Patagonia > Neuquén > Neuquen Basin > Vaca Muerta Shale Formation (0.99)
- South America > Argentina > Patagonia > Neuquén > Neuquen Basin > Loma Campana Field > Vaca Muerta Shale Formation (0.99)
- South America > Argentina > Patagonia > Neuquén > Neuquen Basin > Loma Campana Field > Lower Agrio Formation (0.99)
- (31 more...)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning (0.34)