Low injected fracturing fluid recovery has been an issue during flowback period that is highly impacted by the fracture closure behavior. Although existing flowback models consider fracture closure volumetrically, they do not represent the true situation of non-uniform fracture closure. In this paper, we proposed a coupled geomechanics and fluid flow model for early-time flowback in shale oil reservoirs. The fluid flow model is coupled with an elastic fracture closure model through finite element methods. In this study, three stages are modeled: fracture propagation, well shut-in and flowback. Cohesive Zone Method (CZM) has been used for modeling fracture propagation. The presented model distinguished the propped part from the unpropped part of the fracture. At the beginning of flowback, the proppants may not be completely compacted in early shut-in time. Thus, permeability evolution during closure is tracked using a smooth permeability transition function. The numerical results have shown that fracture closure during the flowback period is often not uniform. While the uniform fracture closure leads to maximum fracturing fluid recovery, an aggressive pressure drawdown strategy may damage fracture connectivity to the wellbore. An integrated flowback model enables modelling nonuniform fracture closure in a complex fracture network. This study highlights that by choke/pressure drawdown management, operators can influence fluid recovery and even maintain high fracture conductivity. Furthermore, the methodology presented in the paper can also be used for inverse analysis on early flowback data.
Hydraulic fracturing is a typical and vital technique applied in shale gas reservoir development. Numerical simulation used to be a common tool to optimize the parameters in hydraulic fracturing design determining the stage numbers, injection pressure, proppant amount, etc. However, the current understanding of shale gas storage and transport mechanism (e.g. adsorption/desorption, diffusion) is basically adopted from the lessons learned from coal seams through past experience, which might not help an efficient numerical simulation development.
In this study, how artificial intelligence assisted data driven models assist the hydraulic fracturing design in shale gas reservoir is discussed. It starts by collecting field data and generate a spatial-temporal database including reservoir characteristics, operational/production information, completion/stimulation data and other variables, Neural Network models are then developed to study the impacts of all parameters on gas production as well as perform history matching of the field history. The AI assisted model with acceptable matching of field data can be used to model different hydraulic fracturing design scenarios and provide predictions on well production.
The current scheme for developing shale reservoirs necessitates special considerations while estimating the reserve. While reservoir characteristics lead to an extended infinite acting flow regime, completion schemes could result in a series of linear flows. Therefore, the initial linear flow does not have to be followed by a boundary-dominated flow. Overlooking this observation leads to unphysical Arps’ exponents and overestimations of the Estimated Ultimate Recovery (EUR). We are proposing a workflow to overcome these challenges and honor the inherited uncertainty while using the classic
Out-Of-Sequence (OOS) Fracturing can potentially maximize reservoir contact and fracture conductivity/connectivity by creating fracture complexity via reducing the stress anisotropy. It is initiated by fracturing two "book-end" frac stages (Outside Fracs), followed by a ‘middle" stage (Centre Frac) between them. The Center Frac is theorized to utilize the reduced stress anisotropy to activate pre-existing failure surfaces oriented at various azimuths and dip angles, thereby connecting bi-wing fractures to planes of weakness (natural fractures/fissures/faults/joints/cleats) and resulting in a complex fracture network that enhances connectivity and fracture area within the Stimulated Reservoir Volume (SRV). OOS Fracturing can mitigate possible issues in treatments aiming at creating fracture complexity, including zipper frac (fracture tip interference and blunting inhibiting fracture extension), modified zipper frac (risks of well bashing and fractures growing asymmetrically opposite of the induced stress from prior stage in the adjacent well), simultaneous frac (middle clusters experiencing larger stress interference inhibiting their growth), and high-rate fracturing (risk of cluster erosion reducing the limited entry effect and premature screenout due to inconsistent diversions inside fractures).
Since its inception in early 2010s, OOS Fracturing has not gained considerable attention due to previously-existing operational limitations in fracturing out-of-sequence. It is reported to have been field tested in Western Siberia in 2014 with claimed well performance success. Operational limitations of the system employed in that trial is believed to have prevented its commercial development at that time. With the advent of Multicycle Sleeves and Shift-Frac-Close operation with a single Bottom-Hole Assembly to open and close sleeves, previous operational limitations of OOS Fracturing have been resolved. OOS Fracturing has since been trialed in three formations in Western Canada (2017/2018). This work analyzes the fracture treatment pressures and well performance of these trials.
Five OOS Fracturing trials in these three formations reveal that normalized 15-month/18-month production from out-of-sequence-fractured wells outperform that of sequentially-fractured offsets, with similar formation properties and treatment designs. Instantaneous Shut-In Pressures (ISIP) of Centre Frac are generally higher than that of either Outside Fracs. Breakdown pressures for Centre Fracs exhibit a mixed trend, confirming that reducing stress anisotropy could lower the breakdown gradient (based on Kirsch Equation) if rock fabric permits. Well performance and treatment pressures appear to be more sensitive to Centre Frac proppant tonnage/fluid volumes and uneven sleeve spacing.
This is the first attempt in analyzing the five OOS Fracturing trials, with encouraging well performance and operational execution in conventional reservoirs where it was deployed. Despite uneven sleeve spacing, depletion due to offset production, and less favorable geomechanical properties (high Poisson’s Ratio and low Young’s Modulus), field trials produced favorable results. True potential of non-sequential fracturing is potentially more promising in unconventional reservoirs with formation properties more conducive to complex fracture generation.
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.
Inter-well communication in unconventional reservoirs has received huge attention due to its significant effects on well production. Though it has long been a known side effect of hydraulic fracturing, well interference has become more prominent and frequent as the industry moves to larger completion designs with closer well spacing and infill drilling. Fracturing of infill wells ("child" wells) directly places the older adjacent producing wells ("parent" wells) at risk of suffering premature change in production behavior. Some wells may never fully recover and, in worst cases, permanently stop producing after taking severe frac hits.
This paper presents an automatic data-driven workflow developed to identify inter-well interference events and their impact on EUR (estimated ultimate recovery) based on changes in the well productivity trend. The innovative approach of the workflow is the ability to automatically analyze interference using the complete production history for all wells in a field, using routinely collected data and without introducing human bias in the derivation of the results, instead applying a consistent criteria. The final result is a comprehensive collection of all well interference events occurred in a field, which may be used as a training set for statistical and machine learning based models aiming at predicting such events.
First, the automatic identification of anomalies in the well behavior was developed and criteria set to label the interference events. Next, probabilistic simulations are run to forecast multiple scenarios to quantify the impact of a well interference event reported in terms of change in cumulative oil production. Finally, every event is analyzed in the overall context of field operations, in an attempt to present possible causes which may explain the change of production behavior.
The combination will create one of the Haynesville Shale’s top gas producers, tripling Comstock’s Haynesville-Bossier acreage. After a drop in drilling activity in recent years, the Haynesville shale has become a hot area for natural gas production in the US, and companies are looking to bolster their positions in the area.
A marked change from a decade ago, Appalachia, the Permian, and the Haynesville now represent almost half of total US gas production, EIA reports. BP To Buy US Shale Assets From BHP for $10.5 Billion BP ends a year of speculation as to who will buy BHP Billiton’s much-coveted US unconventional business, transforming its Lower 48 portfolio in the process. Drilling and completion expenditure and activity is projected to show multiyear double-digit growth from 2018–2022 despite a flattening of rig count increases. After a drop in drilling activity in recent years, the Haynesville shale has become a hot area for natural gas production in the US, and companies are looking to bolster their positions in the area. In drawdown management, operators can exert control over the downhole flow pressure, reservoir pressure, and choke size to avoid estimated ultimate recovery (EUR) losses.
The combination will create one of the Haynesville Shale’s top gas producers, tripling Comstock’s Haynesville-Bossier acreage. Indigo Natural Resources, Aethon Energy, and Rockcliff Energy are among the most active operators in the revived Haynesville Shale of North Louisiana and East Texas. And most people outside of the region likely have never heard of them. After a long cooling off period, this dry-gas shale play is once again red hot. The rebrand follows the completion of BP’s $10.5-billion purchase of BHP’s assets in the Permian, Eagle Ford, and Haynesville.