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Decline-curve analysis is one of the more widely used forms of data analysis that evaluates well behavior and forecasts production and reserves. This paper presents technologies that apply DCA methods to wells in an unbiased, systematic, intelligent, and automated fashion. The resistivity index (RI) of Fontainebleau and Bentheimer sandstones was investigated at ambient and reservoir pressures down to low water saturations. The RI measurements show that both sandstones display Archie behavior at elevated pressure. This paper presents experimental and field-case studies with a sandstone-acidizing treatment designed to retard the hydrofluoric acid reaction rate and enable single-stage treatment.
Decline-curve analysis is one of the more widely used forms of data analysis that evaluates well behavior and forecasts production and reserves. This paper presents technologies that apply DCA methods to wells in an unbiased, systematic, intelligent, and automated fashion. In the Marcellus Shale, ROP improved 61% and 39% and drilling performance, measured as hours on bottom, improved 25%. With their gee-whiz—albeit artificial—intelligence, robots may be the industry’s answer to jobs deemed dangerous, dirty, distant, or dull. For the upstream industry, where improvement in efficiency or production can drive significant financial results, there is no question that the size of the digital prize is huge.
Decline-curve analysis is one of the more widely used forms of data analysis that evaluates well behavior and forecasts production and reserves. This paper presents technologies that apply DCA methods to wells in an unbiased, systematic, intelligent, and automated fashion. An intelligent drilling optimization application performs as an adaptive autodriller. In the Marcellus Shale, ROP improved 61% and 39% and drilling performance, measured as hours on bottom, improved 25%. With their gee-whiz—albeit artificial—intelligence, robots may be the industry’s answer to jobs deemed dangerous, dirty, distant, or dull.
Operators and investors are interested in finding better metrics to evaluate the production performance of unconventional multi-fractured horizontal wells (MFHWs). This paper discusses the use of cumulative production Ratio curves, normalized to a given reference volume in time (e.g. 12-month cumulative production) for different unconventional plays in North America to investigate the median trend for each play, and investigate the median ultimate recovery per play. The selection of using 12-month cumulative production as a reference volume as a normalization parameter is discussed.
Historical production data from thousands of MFHWs in unconventional plays in the US (Bakken, Barnett, Eagleford, Fayeteville, Haynesville, Marcellus and Permian) and Canada (Bakken, Duvernay, Montney and Horn River) was used to calculate normalized cumulative production curves for the primary fluid, using different cumulative reference volumes at different points in time (e.g. 6, 12, 24, 36, 48 and 60 months). The observed trends for each of the selected plays were studied using data analytics tools. A two-segment hyperbolic decline was used to match the median production trend to estimate the long-term performance of each play.
Depending on the data variance, some plays exhibit more clear trends than others. By using normalized cumulative production curves, general profiles for each play were generated and compared. These Cumulative Production Ratio Profiles (CPRP) were extended using a two-segment hyperbolic equation to determine the Expected Ultimate Recovery Ratios (EURR) per play.
Once a well in a region has been on production for a minimum duration equal to the reference time (e.g. 12 months), two results are readily determined: a) the EUR, and b) the production profile. The EUR is obtained simply by multiplying the appropriate EURR by the well’s 12-month cumulative production; and the production profile is obtained by using the CPRP (cumulative production ratio profile) of the play and multiplying it by the 12-month cumulative production of the well and converting the results to daily rates.
This cumulative plot serves as a normalized typewell for the region and can be used to guide the production forecasts of wells with a short production life.
Analytical rate transient analysis (RTA) techniques are widely adopted for analyzing production data obtained from hydraulically fractured horizontal wells in tight or shale reservoirs. However, for a detailed characterization of the uncertain distributions of those complex heterogeneous and multi-scale fractures, numerical simulation and assisted history-matching approaches are often preferred. Generally, RTA results can be used to constrain the initial distributions of fracture properties (e.g., transmissivity, aperture, or intensity). A set of initial models are then perturbed during the assisted history-matching step. Unfortunately, the final updated models may deviate substantially from the initial RTA estimates; moreover, specific information regarding the flow regimes is not incorporated directly into the history-matching step. In this paper, a new assisted history-matching workflow is presented, where RTA results are used to constrain not only the initial DFN models, the interpreted flow regimes are also used to formulate a localization scheme for more efficient updating of the pertinent DFN model parameters. The outcome is an ensemble of DFN realizations that are calibrated to both geologic and dynamic production data.
First, RTA interpretations and other pertinent geological data are used to infer the prior probability distributions of the unknown fracture parameters, from which an ensemble of initial DFN models is sampled. Next, the DFN models are subjected to numerical multiphase flow simulation; the predicted production profiles are compared with the actual historical production data. Finally, the fracture parameters are adjusted following an indicator-based probability perturbation method, which is capable of minimizing the objective function and reducing the uncertainties in the unknown fracture parameters simultaneously. A key feature is that the flow regimes identified from RTA are used to formulate a localization strategy, where individual segments of the production data is used to tune only a specified subset of the unknown model parameters. The adoption of localization strategies in other settings has been demonstrated to improve the convergence behavior of such ill-posed inverse problems.
In a case study, the method is applied to characterize the probability distributions of four parameters in a multifractured shale gas well: primary fracture transmissivity, aperture of the secondary fracture, transmissivity of the secondary induced fracture and global fracture intensity. Results of the sensitivity analysis reveal that the production performance is most sensitive to these particular parameters. Their probability distributions are updated following the proposed approach to match the production history. Multiple realizations of the DFN model are sampled.
A probabilistic approach facilitates the representation of uncertainties in fracture parameters via multiple equally-probable DFN models and their corresponding upscaled flow-simulation models. A more comprehensive and robust approach is presented for integrating specific RTA interpretations and estimations into various steps of the history-matching process.
Clarkson, Christopher R. (University of Calgary) | Williams-Kovacs, Jesse (University of Calgary and Sproule Associated Limited) | Zhang, Zhenzihao (University of Calgary) | Yuan, Bin (University of Calgary) | Ghanizadeh, Amin (University of Calgary) | Hamdi, Hamidreza (University of Calgary) | Islam, Arshad (Baytex Energy Corp.)
Recently it has been demonstrated that rate-transient analysis (RTA) performed on flowback data from multi-fractured horizontal wells (MFHWs) can provide timely estimates of hydraulic fracture properties. This information can be used to inform stimulation treatment design on upcoming wells as well as other important operational and development decisions. However, RTA of flowback data may be complicated by rapidly changing operating conditions, dynamic hydraulic fracture properties and multi-phase flow in the fractures, complex fracture geometry, and variable fracture and reservoir properties along the MFHW, amongst other factors. While some constraints on RTA model assumptions may be applied through a carefully-designed surveillance and testing program in the field (e.g. to constrain fracture geometry), still others require laboratory measurements.
In this work, an integrated flowback RTA workflow, designed to reduce uncertainty in derived hydraulic fracture properties, is demonstrated using flowback data from MFHWs producing black oil from low-permeability reservoirs in the Montney and Duvernay formations. The workflow includes rigorous flow regime identification used for RTA model selection, straight-line analysis (SLA) to provide initial estimates of hydraulic fracture properties, and model history matching of flowback data to refine hydraulic fracture property estimates. The model history matching is performed using a recently-introduced semi-analytical, dual-porosity, dynamic drainage area (DP-DDA) model that incorporates primary (propped) hydraulic fractures (PHF) as well as a dual-porosity enhanced fracture region (EFR) with an unpropped (secondary) fracture network. Inclusion of both the PHF and EFR components addresses the need to incorporate both propped and unpropped fractures and fracture complexity in the modeling. The DP-DDA model is constrained using estimates of propped and unpropped fracture permeability (measured as a function of stress), and unpropped fracture compressibility values, obtained in the laboratory for Montney and Duvernay core samples. Use of these critical laboratory data serves to improve the confidence in the modeling results.
The case studies provided herein demonstrate a rigorous workflow for obtaining more confident hydraulic fracture property estimates from flowback data through the application of RTA techniques constrained by both field and laboratory data.
Aimed at sharing the unconventional wisdom gained from a hydraulic fracturing monitoring case study in the Montney tight gas play, the work showcases the ability of 4D modeling of collective behaviors of microseismic events to chase the frac fluid and navigate the spatiotemporal fracture evolution. Moreover, microseismicity-derived deformation fields are integrated with volumetric estimates made by rate transient analysis to calibrate spatially-constrained SRV models. Through the case study, we give evidence of fracture containment, evaluate the role of natural fractures and the use of diverting agents, estimate cluster efficiencies, conduct analytical well spacing optimization, model productivity decline induced by communication frac-hits from offsets, and provide contributing fracture dimensions and numerical production forecasts. To support the interpretations, we supplement the work by the results of 3D physics- based analytical modeling and multi-phase numerical simulations, and the findings are then validated using two extensive datasets: production profiles acquired by fiber optic DAS, and reservoir fluid fingerprints extracted from mud logs. Besides describing the evolution of seismicity during the treatment, the applied integrated fracture mapping process gives a more reliable and unique SRV structure that streamlines forward modeling and simulations in unconventional reservoirs as well as contributes to solving inverse problems more mechanistically.
Unconventional plays present a challenging case to design an optimized stimulation program and to maximize reservoir contact and hydrocarbon production. In this regard, conducting a reliable well spacing optimization study demands realistic, unique estimates of fracture pattern. This work applies an integrated technique to a multi-well case study in Permian Basin to extract fracture dimensions based on microseismicity-derived behavioral fracture maps, while honoring the RTA estimates of fracture volume. The fracture dimensions are then used to conduct analytical and numerical studies to find the optimal well spacing design in the target formation. The numerical simulations in two stacked and staggered scenarios, while honoring the microseismicity-constrained contributing fracture heights, confirm that the staggered development causes only a marginal decrease in the well performance and contributes to a higher vertical sweep efficiency. Furthermore, comparing the approximations of fracture propagation direction, constructed based on the spatiotemporal analysis of microseismic events, with those achieved through seismic moment tensor inversion confirms that the collective behavior analysis can be applied to map fracture spatial evolutions and promote the spatial connectivity of SMTI-based models.
Enterprise Products Partners publicly acknowledged the deep slump in pipeline demand out of the Permian Basin by canceling a project at a time when most producers have been quietly postponing US projects. Company cites COVID-19-driven decline in fuel demand in move following announcement of Australian layoff plan as latest cost-cutting measure. Advanced machine-learning methods combined with aspects of game theory are helping operators understand the drivers of water production and improve forecasting and economics in unconventional basins. Decline-curve analysis is one of the more widely used forms of data analysis that evaluates well behavior and forecasts production and reserves. This paper presents technologies that apply DCA methods to wells in an unbiased, systematic, intelligent, and automated fashion.