Gas-assisted plunger lift (GAPL) could be an effective and economically favorable artificial lift (AL) method to be considered during the AL life cycle for North American shale wells. The main advantage of GAPL is that it improves the well production by reducing liquid fallback and boosts the plunger efficiency through gas injection and increases the gas lift efficiency by assisting in delivering the slugs to the surface. The objective of this study is to capture the GAPL dynamic behavior through a transient multiphase flow simulator. The entire GAPL production cycle was modeled, including plunger fall, gas injection, pressure buildup, and production. First, the GAPL well production history was analyzed to evaluate the well operating condition. Then, a transient simulator was used to model the well flow behavior and production performance with GAPL. The study demonstrated the GAPL impact on flowing bottomhole pressure and the improvement in the well productivity.
A Delaware Basin well case study demonstrates the benefits of dynamic modeling and provides a comprehensive comparison between dynamic simulation results and field data. The simulation work provides insights into the fluid flow, GAPL behavior, and pressure and rate transients of a GAPL well.
The modeling results were validated against field data. A commercially available transient multiphase flow simulator was used and produced outcomes that were in alignment with field data collected. The dynamic plunger cycles were reproduced in the simulation, and the results showed the benefits of GAPL in a typical shale oil well. This could extend the gas lift life by delaying the transition to rod pumps or potentially act as an end-of-life AL solution. In the long term, this reduces the overall AL life cycle cost. The use of transient simulation helps validate AL design concepts, especially for unconventional wells where the flow behavior is very dynamic. This study encourages the use of this analysis in the AL selection workflow to help optimize the overall AL life cycle cost and maximize the net present value (NPV).
Production from shales can be dependent on many things, including multiple reservoir properties, drilling, completion and production methods. Designs and analyses often focus on drilling and completion issues, such as number of stages, wellbore length and fracture properties, such as conductivity, length, spacing and complexity. As a result, many aspects of the reservoir, fractures and production methods can be significant. Flow from the reservoir to production points is driven by pressure drops. If an entire well including fractures and surrounding reservoir is considered as a single system, then the production behavior is driven by the magnitude of three pressure drops and corresponding resistances to flow in the system. Those that need to be considered are: pressure drop between the reservoir and the fractures, pressure drop along the fractures to the wellbore perforations and pressure drop along the wellbore to the pump inlet or tubing head. Different aspects of the well/fracture/reservoir system become important, or unimportant, depending on the relative magnitude of these pressure drops and resistances to flow. For example, many people believe that fractures should be as long as possible assuming they can be restricted to zones of interest and do not interfere with other wells and/or fractures. However, since the pressure along a fracture increases as you move further away from the wellbore, increasing fracture length can have diminishing returns for reservoirs with small reservoir to fracture pressure drops and/or low fracture conductivities.
Improved field management for monitoring, estimating zone productivity/injectivity, and controlling wells with intelligent completions can broaden application of advanced well designs. We have developed a coupled Simulation-Surface Network modeling workflow to evaluate the potential benefit of intelligent injection profile control with a focus on reactive vs proactive control for Gulf of Mexico (GOM) Deepwater Enhanced Oil Recovery (EOR) schemes. The developed injection control solution can be consistently applied to gas, water injection, and gas followed by water injection, to evaluate relative impacts of intelligent injectors on each option. We did this by defining rules for both proactive and reactive injection ICV controls for a GOM Deepwater Wilcox multilayered reservoir. Proactive controls, based on reservoir zone characteristics, pore volume injected, and recoverable pore volume, are dependent on a static reservoir model realization. Proactive control results demonstrate a diminishing return as we begin to observe fluid breakthroughs that results in part from the inevitable uncertainty of the original static assessment so there should be a benefit in reassessing optimal pore volume injection based on reservoir model updates. Reactive control strategies based on measured production response is a challenge in terms of linking injection control events to production responses that are time-lagged and incomplete for understanding gas and water breakthrough. The integrated model captures the effects of topside facilities, risers, flowlines, pressure/temperature at manifolds and topside, seafloor booster pump performance, wellheads, and wellbore to reservoir interactions and ICV controls to provide a realistic evaluation of achievable development alternatives outcomes.
Ryan, M. (Baker Hughes, a GE Company) | Gohari, K. (Baker Hughes, a GE Company) | Bilic, J. (Baker Hughes, a GE Company) | Livescu, S. (Baker Hughes, a GE Company) | Lindsey, B. J. (Baker Hughes, a GE Company) | Johnson, A. (Murphy Oil Company) | Baird, J. (Murphy Oil Company)
Development of unconventional reservoirs in North America has increased significantly over the past decade. The increased activity in this space has provided significant data with respect to through-tubing drillouts which had previously not been attainable. This paper is focused on using the field data from the Montney and Duvernay formations along with laboratory data and numerical modeling to understand the hole cleanout associated with through-tubing drillouts of frac plugs.
Initially, an extensive full-scale flow loop laboratory testing program was conducted to obtain data on debris transportation for hole cleanout during through-tubing applications. The testing was conducted on various coiled tubing (CT)-production tubing configurations using various solid particles. The laboratory data was used to develop empirical correlations needed for a transient debris transport model. This model was then used for frac plug drillouts to ensure successful hole cleaning in actual field applications. Computational fluid dynamics (CFD) modelling was also used to further understand and quantify the differences between the laboratory data, field data and transient debris transport model results.
The objective of the work conducted was to gain a better understanding of debris transport and validate the empirical modelling approach developed for hole cleaning. The validation process was conducted in several stages. The first stage was to validate the laboratory data against the Montney and Duvernay field data. The second stage was to verify the results obtained from the empirical model against the results obtained from a computational fluid dynamic model. The results from both modelling approaches were lastly compared to the field data. All these results challenge the current industry's understanding and best practices for through-tubing drillouts in the Montney and Duvernay formations. With the contentious increase of lateral lengths and higher stage counts, the process of drilling out frac plugs has become more complex. This study explicitly benefits all operators in their ever-increasing need to understand their frac plug drillout operations to ensure efficient, cost effective, and most importantly, consistent and repeatable results.
While efficient results for frac plug drillout operations have been accomplished to date, the on-going feedback from the field has been the requirement to produce repeatable drillouts. This paper is the first to show a holistic approach for obtaining a transient debris transport model used for through-tubing drillouts of frac plugs. The novelty also consists of the transient debris transport model validation through laboratory data and actual Montney and Duvernay field data.
Cao, Richard (Shell Exploration and Production Co.) | Chen, Chaohui (Shell Exploration and Production Co.) | Girardi, Alejandro (Shell Exploration and Production Co.) | Li, Ruijian (Shell Exploration and Production Co.) | Chowdhury, Nitin (Shell Exploration and Production Co.)
Optimum co-development layout of multiple targets for unconventional reservoirs is extremely challenging due to complex 3-dimentional well interactions, stochastic well performance, complex fracture geometry, dynamic SRV/DRV evolution, heterogeneous rock properties, various operating conditions, and different economic drivers. In this study, an integrated workflow is developed and applied for co-development of multiple targets in Permian unconventional reservoirs. In this workflow, the field pilot and trial measurements, Microseismic, geochemistry measurement, data analytics, detailed geomechanical and reservoir modeling, stochastic multiple history matching and forecast, all combined to quantify the horizontal and vertical interference factors and obtain production profiles for different co-development designs. The stochastic behavior of the well performance is explored from three different aspects: static rock properties, dynamic fracturing, and production. The SRV/DRV evolution are presented as the probability distribution function of half fracture length from Microseismic data and effective drainage half-length from stochastic modeling.
Objectives/Scope: In order to maximize the recovery of hydrocarbons from liquids rich shale reservoir systems, the cause and effect relationships between production and the stimulation methods need to be clearly understood. In this study, we utilize multivariate regression models to narrow down the variables in flow simulation models and their range. We then use the flow simulation model to understand the fractured well production behavior and field wide well performance in a liquids rich petroleum system in the Duvernay Basin.
Methods, Procedures, Process: Statistical models assume no physical relationship between the model parameters and the response variable, which in this case is produced volumes over a period of time. On the other hand, simulation studies incorporate physical mechanisms of flow to model and predict the production behavior. The simulation models, however, fall short of incorporating all the mechanisms contributing to the production behavior in the complex shale gas reservoir. Thus there is a need for integration of statistical approaches of understanding production behavior along with physics based model and simulation approach. We use the statistical methods to identify the important physical mechanisms that control the production.
Results, Observations, Conclusions: Multivariate linear regression analysis of the 6 month produced volume and its relationship with parameters such as fracture fluid volumes used, proppant weight placed, number of stages fractured provides a model with reasonably good correlation. The 6 month produced volumes correlate with large proppant weights, lower fluid placements and greater density of fracture stages. Use of Random Forests machine learning algorithm on the dataset confirms that the total proppant placed, well length completed with fractures have high importance coefficients. In order to examine the well performance using full physical models, fractured well simulations are performed on particular wells using the trilinear model. The trilinear model predictions are then compared against other production analyses and the regression model results for consistency. The models showed that in the absence of stress dependent permeability, the production forecast was much higher. Thus, stress dependent permeability appears to be an important factor in the modeling and prediction of production from liquids rich shale reservoirs.
Novel/Additive Information: In this study we describe a method to understand the production data from a liquids rich shale reservoir, by integrating multivariate linear regression analysis, machine learning algorithms along with physical model simulations. The results are novel and offer a method to validate either approach to understand cause and effect relationships. This approach may be classified as a new hybrid modeling workflow that may potentially be used to optimize stimulation techniques in liquids rich shale reservoirs.
Booking reserves for unconventional, multi-frac wells is a critical business process, but to be done effectively, often requires significant time investment and multiple interpretation techniques. Although reserves can be estimated quickly with decline curve analysis (DCA) alone, the subjectivity in DCA makes it challenging for evaluators to estimate reserves with appropriate levels of uncertainty and maintain consistency between evaluators. The objective of this paper is to present a fast, systematic, yet rigorous methodology for estimating 1P, 2P and 3P estimated ultimate recoveries (EURs) for new wells. This methodology utilizes regression to correlate easy to obtain, early life indicators of well performance to 2P EURs, which have been estimated from more detailed interpretations. Multiple methodologies are presented for estimating 1P and 3P EURs.
Wilson, Glenn (Halliburton) | Marchant, David (Computational Geosciences) | Haber, Eldad (University of British Colombia) | Clegg, Nigel (Halliburton) | Zurcher, Derick (Halliburton) | Rawsthorne, Luke (AkerBP) | Kunnas, Jari (Halliburton)
Ultradeep resistivity logging-while-drilling (LWD) is now a routine service for real-time well landing,geosteering, and reservoir and fluid contact evaluation. Progressing beyond layered earth inversions to three-dimensional (3D) inversions helps improve real-time decisions to deliver better well placement, completion, and production. To this end, the first real-time 3D inversion of ultradeep resistivity LWD data is realized by exploiting the fact that the sensitive volume of a given transmitter-receiver pair is far smaller than the total logging volume. This implies that the global mesh can be decoupled into multiple independent, localized inversion and modeling meshes that are tractable for the efficient solution of the forward and inverse problems in real time using moderate computer resources. The authors' implementation is based on a 3D finite-volume method discretized on locally refined octree meshes. It uses the regularized Gauss-Newton method for minimizing the objective function for data subsets on local inversion meshes, which iteratively update the global mesh. Nonlinear Kalman filtering is applied using prior information on each local inversion mesh from the updated global mesh to introduce new observations optimally. A model study and a case study of trilateral well placement in a mature reservoir in the Norwegian Continental Shelf demonstrate the efficacy of the method. Run times on modest computer resources enable the first real-time 3D inversion of ultradeep resistivity LWD data.
Simões Maciel, Rodrigo (Federal University of Espírito Santo) | Ressel Pereira, Fábio de Assis (Federal University of Espírito Santo) | Fieni Fejoli, Rômulo (Federal University of Espírito Santo) | Leibsohn Martins, André (Petrobras) | Duarte Ferreira, Marcus Vinicius (Petrobras)
Petrobras has faced several challenges concerning inorganic scaling in the Pre-salt cluster. Scale prediction plays an important role on well completion selection and supporting to define better alternatives for chemical injection location. However, predicting scale in wellbores is traditionally performed based on thermodynamical equilibrium of the formation water under static conditions. This strategy leads to conservative results since it neglects hydrodynamics and kinetics of the scaling process. This paper proposes a new approach to predict scaling in downhole conditions. The study seeks to contribute on the comprehension of the effect of fluid flow and equipment geometry variation in the crystal deposition process in intelligent well completion equipment.
Such completion devices act in managing the fluid flow influx from different reservoirs or multiple zones of the same reservoir. Despite the positive aspects of this technology, some authors have been pointing out some problems associated with specific applications of these tools. The most common issues are related to the considerable pressure differential and the occurrence of calcium carbonate (CaCO3) scale. The pressure drop in this tool induces the flash liberation of CO2 from the aqueous solution. Consequently, the chemical equilibrium is displaced towards the direction of precipitation of CaCO3 in the flow stream. This paper proposes a new approach to predict scaling in downhole conditions and aims to quantitatively evaluate the calcium carbonate precipitation on the smart completion element internal surfaces. Computational Fluid Dynamics (CFD) along with discrete phase modeling (DPM) is employed to simulate the transport and adhesion of the calcium carbonate crystals on the device. The valves geometries consider the main features observed on the field according to different suppliers, accounting the different possibilities of completion geometries for Brazilian Pre-Salt environment.
The results showed the tendency of scale deposition pointing out hot spots in several different completion accessories at downhole conditions. A better understanding of the scale potential has influenced the decision-making process on the completion design and workover alternatives in the Pre-salt wellbores.
Carbonate Brazilian pre-salt fields have a large number of faults detected by seismic and well data. Nevertheless, because of limitations in seismic resolution, all existent faults cannot be identified. That is one of the main challenges for understanding related heterogeneities (vugs, karst) and the flow behavior. This paper deals with a fault analysis and modeling using an original approach and fault data of three pre-salt reservoirs.
One possible approach for characterizing and modeling the fault network (
The results presented on this article lead us to discuss the importance of how to choose the samples for modeling sub-seismic faults based on the ensemble of seismic faults available. This article answers the question about which available seismic faults we should use for estimating fractal dimension, should we use all available seismic faults near of the reservoir area or use only the faults inside the reservoir contour. After this short discussion on the fractal dimension choice from a spatial distribution point of view, the impact of this choice on flow was illustrated. The sub-seismic fault models were modeled using different fractal dimension. Subsequently, an upscaling step using analytical upscaling (
Characterizing sub-seismic faults has a major impact on the overall flow behavior of the field. The chosen methodology has been applied only on synthetic cases but never published using real data. This work will interest a practicing engineer. The fault network of these neighbor reservoirs allows us to illustrate the importance on the choice of fractal dimension for characterizing the fault network and its impact on the subseismic models and fluid displacement, consequently on production.