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
The most common stimulation technique of shale gas production is multistage hydraulic fracturing. However, the implementation of the technique brings in new formation damage considerations. Large quantities of water-based fracture fluids, over 75% of the injected volume, usually left unrecovered at the start of production that leads to permeability reduction and low productivity. Accidently, some operators found an improvement in gas recovery after shut in the wells after flowback due to pipeline restriction. They called this behavior as the soaking effect.
This study presents a workflow to evaluate the effect of the soaking process on the well performance after the hydraulic fracturing process in actual field cases. Waterflow back analysis was conducted for 21 well to estimate the effective fracture volume before and after the soaking process. Rate transient analysis (RTA) was conducted on the production data to estimate the stimulated reservoir volume (SRV) in each well. SRV and the enhanced recovery were correlated to the soaking time. Decline curve analysis for water and gas flow rates were conducted to estimate the estimated ultimate gas and water recovery (EURg, and EURw) before and after the soaking process.
An increase in the gas flow rate was observed with soaking time with low water production. SRV increased with the soaking process up to 53% of its initial value with shut-in the well for 180 days. EURw decreased by 52 % of its value before the soaking process, while EURg increased by 48%. Shut-in the well before gas-kick off after hydraulic fracturing operations negatively impact the well performance and the gas production can decrease by 22% even after soaking process for 315 days.
This study will present a methodology to evaluate the soaking process, and recommendations to improve the impact of the soaking process on well performance.
As unconventional reservoirs have become more challenging to develop and predict, understanding well performance has proven to be essential for driving value. Though there have been continuous advancements in well performance analysis and production forecasting for unconventional reservoirs that exhibit prolonged transient flow conditions, there is a still a gaping need for robust and scalable methods which are usable from a practical standpoint, considering data availability and other uncertainties. Traditional forecasting methods (decline curve analysis and its variants) are often not fully representative and impacted by surface operations such as constrained flow, choke changes etc. or subsurface events such as well interference, frac hits, depletion below saturation pressure, etc. Analytical and numerical modeling methods address this issue by applying first principles and simplified physics for integrating flow diagnostics and time-rate-pressure analysis. However, this is often quite interpretive through manual analyses that are not scalable, requiring additional reservoir inputs that are often not collected or known for all wells. Consequently, these methods are not suitable for large scale, repetitive forecasting purposes. We desire a reliable, consistent and scalable well performance analysis method that can work with routinely measured data for most unconventional wells (i.e.
Steam Assisted Gravity Drainage (SAGD) is a complex process and often requires more control relative to conventional applications during production operations. Flow Control Devices (FCDs) have been identified as a technology that offers improved efficiency of the process while simplifying the operations. The first FCD completions were installed in SAGD wells in Canada over a decade ago with the intention of improving the steam chamber conformance and reducing the steam-oil ratio (SOR). While it is widely understood that FCD completions, for the most part, have helped achieve the desired uplift for SAGD producers, further optimization could be made on future completion designs and operation strategy by looking at actual performance data from previous installations. The objective of the study was to obtain key design parameters and considerations for future FCD completion designs.
The majority of FCD completions in MacKay River were tubing deployed, installed in previously producing wellbores (retrofit). This study looks at 11 wells that were completed with a Baker Hughes FCDs. The analysis was broken down into 2 segments: production analysis and modelling. Production strategy implemented for each well was taken into account to eliminate variances. The modelling used a combination of steady state simulation (presented in this paper) and numerical simulation (to be presented in part II).
The study showed that TD FCDs improve the performance of SAGD well pairs when implemented in the appropriate candidate wells. An important outcome was the development of a candidate wells’ selection criteria, to ensure the retrofit completion improved performance and did not exacerbate other problems. Furthermore, design consideration were identified to improve the performances of future TD FCD installations.
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.
Hirschmiller, John (GLJ Petroleum Consultants Ltd.) | Biryukov, Anton (Verdazo Analytics) | Groulx, Bertrand (Verdazo Analytics) | Emmerson, Brian (Verdazo Analytics) | Quinell, Scott (GLJ Petroleum Consultants Ltd.)
This machine learning study incorporates geoscience and engineering data to characterize which geological, reservoir and completion data contribute most significantly to well production performance. A better understanding of the key factors that predict well performance is essential in assessing the commercial viability of exploration and development, in the optimization of capital spending to increase rates of return, and in reserve and resource evaluations.
Machine learning models provide an objective, analytical means to interpret large, complex datasets. Generally, such models demand large databases of consistently evaluated data. As geological data is interpretive, often varying from one geologist to another, or from one pool to another, it can be difficult to incorporate geological data into regional machine learning models. Consequently, efforts to use machine learning in the oil and gas industry to predict well performance are often focused exclusively on engineering completion technology. However, this case study has utilized a regional geological Spirit River database with consistent petrophysical evaluation methodology across the entire play. This geological database is complemented with public completion and fracture data and production data to build predictive models using inputs from all subsurface disciplines.
Redundancies in the data were identified and removed. Features explaining a significant proportion of the variance in production were also removed if their effect was captured by more fundamental, correlated features that were more straightforward to interpret. The dataset was distilled to 13 key features providing predictions with a similar precision to those obtained using the full-featured dataset.
The thirteen features in this case study are a combination of geological, reservoir and completion data, underlining that an approach integrating both geoscience and engineering data is vital to predicting and optimizing well performance accurately for future wells.
System instability prediction is essential when designing a production system and/or providing operational adjustment to maintain a stable production. The conventional system Nodal Analysis articulates that the system is unstable to the left of the minimum of the Outflow Performance Relationship (OPR) curve where the well loads up. However, recent data shows that there are stable production points on the left of the minimum of the OPR curve, especially for low permeability shale plays. In this work, a new practical model is presented for both conventional and unconventional wells using Nodal Analysis with a novel approach.
The new approach is based on the derivative analysis of the inflow performance relationship (IPR) and OPR at a nodal point of the bottom hole. Perturbation analysis is used to facilitate the explanation of the new model. It shows that the system is stable when the absolute value of slopes or derivatives of the IPR is greater than that of OPR. To evaluate this concept, transient numerical simulations were conducted using a commercial transient simulator at various IPR conditions, including different permeabilities, for both vertical and horizontal wells. Meanwhile, the concept is also compared with available experimental and field data.
The transient simulation and the available data presented in this study demonstrate that there are stable production operating points on the left of the minimum of the OPR curve. The system stability also depends on the reservoir permeability, i.e., the flow rate corresponding to the onset of instability decreases with decreasing permeability. The new approach predicts this trend well. Overall, the new model matches well with observation from the experiments, field data, and the transient numerical simulations.
The main goal of production logging is to evaluate the well or reservoir performance. The Delaware Basin presented a challenging scenario for Anadarko due to the company’s tankless development approach. Sand filtration was identified as a perfect fit for this application. Natural gas hydrates are considered to store vast amount of natural gas trapped in the subsurface worldwide. However, gas hydrates are yet to be commercialized due to technical challenges in the production technology.
This paper summarizes a technology using SMP to provide downhole sand control in openhole environments. With multistage operations becoming the industry norm, operators need easily deployable diversion technologies that will protect previously stimulated perforations and enable addition of new ones. This paper reviews several aspects of the use of in-stage diversion. Development of a new polymer composite that degrades via hydrolysis in hot water or brine holds potential for use in structural applications for intervention-less downhole tools. The polymer-injection project in the Dalia field, one of the main fields of Block 17 in deepwater Angola, represents a world first for both surface and subsurface aspects.