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Abstract Characterization of hydraulic fracture system in multi-fractured horizontal wells (MFHW) is one of the key steps in well spacing optimization of tight and shale reservoirs. Different methods have been proposed in the industry including core-through, micro-seismic, off-set pressure data monitoring during hydraulic fracturing, pressure depletion mapping, rate-transient analysis, pressure-transient analysis, and pressure interference test. Pressure interference test for a production and monitoring well pair includes flowing the production well at a stable rate while keeping the monitoring well shut-in and recording its pressure. In this study, the coupled flow of gas in hydraulic fractures and matrix systems during pressure interference test is modeled using an analytical method. The model is based on Laplace transform combined with pseudo-pressure and pseudo-time. The model is validated against numerical simulation to make sure the inter-well communication test is reasonably represented. Two key parameters were introduced and calculated with time using the analytical model including pressure drawdown ratio and pressure decline ratio. The model is applied to two field cases from Montney formation. In this case, two wells in the gas condensate region of Montney were selected for a pressure interference test. The monitoring well was equipped with downhole gauges. As the producing well was opened for production, the bottom-hole pressure of the monitoring well started declining at much lower rate than the production well. The pressure decline rate in the monitoring well eventually approached that of the producing well after days of production. This whole process was modeled using the analytical model of this study by adjusting the conductivity of the communicating fractures between the well pairs. This study provides a practical analytical tool for quantitative analysis of the interference test in MFHWs. This model can be integrated with other tools for improved characterization of hydraulic fracture systems in tight and shale reservoirs.
Shear sonic travel time (DTS), along with compressional sonic travel time and bulk density are required in order to estimate rock mechanical properties which play an important role in fracture propagation and the success of hydraulic fracture treatments in horizontal wells. DTS logs are often missing from the log suite due to their costs and time to process. The following study presents a machine learning procedure capable of generating highly accurate synthetic DTS curves. A hybrid convolutional-recurrent neural network (c-RNN) was chosen in the development of this procedure as it can learn sequential data which a traditional neural network (ANN) cannot. The accuracy of the c-RNN was superior when compared to that of the ANN, simple baselines and empirical correlations. This procedure is a cost effective and fast alternative to running DTS logs and with further development, has the potential to be used for predicting production performance from unconventional reservoirs.
Abstract Recently machine learning has being extensively deployed for oil and gas industry for improving result and expedite process. However, the black box models do not explain their prediction which considered as a barrier to adopt machine learning. This paper is about optimizing hydraulic fracture with machine learning methods and making informative decision with interpreting machine learning model. The solution can show that it could save over million dollars per well and improve well performance significantly. Interestingly, the machine leaning explainability approach was utilized to explain and measure the reason behind of why some wells are performing better than other and vice versa. Hydraulic fracturing modeling and optimization in tight oil and unconventional reservoir requires substantial geological modeling, fracture design, post-fracture production simulation with excessive sensitivity analysis due to complexity and uncertainty in the nature of data. These types of studies are computationally and monetarily expensive. Furthermore, digital oil technology has facilitated the process of data gathering enabled operators to have access to huge amount of data. Common approaches are no longer suitable to handle this pile of data but machine learning methods could be successfully utilized for this purpose. In this paper, a variety types of advanced machine learning methods including linear regression, Random forest, Gradient Boost, XGBoost, Bagging, ExtraTrees and neural network were employed to optimize well completion in Montney formation. The objective was to create a robust predictive model capturing all the effective operational well parameters (features) capable of optimizing the first 12 months cumulative of equivalent well production. Special Individual Conditional Expectation (ICE) plots and Partial Dependency plots(PDP) were used to depict how HF completion features influence the prediction of a machine learning model. Furthermore, a novel approach was employed to explain the model prediction of an existing well by computing the contribution of each feature to the prediction. Over 1838 hydraulically fractured (HF) wells producing from 2008 till 2019 in Montney formation have been considered for this analysis. The outcome of Explanatory Data Analysis (EDA) revealed that well production performance has not been improved despite of continues enhancement of hydraulic fracture parameters such as proppant injected volume, length of stimulated horizontal wells, and number of stages per well in the course of two years. This finding raises the concern of whether operators are properly optimizing completion design. After comparing all machine learning methods, Random Forest method was chosen as the most appropriate and accurate method to proceed for further analysis. ICE and PDP plots helped to understand the impacts of different fracturing features on production for individual well in addition to define optimum operation features on Montney Formation. Furthermore, quantifying of each feature’s impact on individual well production and linking it to an economic model, we were able to demonstrate potential profit and loss for each well. The model suggests that some wells could have achieved over $1 million extra profit during the first 12-months of production. In this study, not only a reliable predictive data-driven model has been built for hydraulically-fractured wells in Montney formation, but also a comprehensive workflow of sensitivity and explainatability analysis has been introduced to obtain an optimized fit-to-purpose well completion design.
Data analytics methods (data mining and artificial intelligence) have been used to model the production from unconventional plays. However, the performance of predictive models derived from these techniques has not been very successful. Their weak performance can be attributed to several reasons, including the poor quality of field data as well as using too few well characteristics and well completion data as the input parameters. This study uses the capabilities of big data analytics, including data mining and machine learning, to develop a model for a typical unconventional gas play, the Montney formation in the province of British Columbia, Canada. In the research demonstrated here, we first built a database containing well completion design parameters and well characteristics (16 features) for 603 horizontal wells in the Montney formation. We then assessed the relationship between various parameters and gas production rate and determined a subset of the most significant parameters. Finally, we conducted a comparative study among artificial neural network models with different training algorithms to find the best algorithm for predicting the average gas production rate over the first year of operation. The benchmarks for comparison of the algorithms were the root mean square error (RMSE), mean absolute error (MAE), mean relative error (MRE), correlation coefficient (R2), and execution time.
A design of experiments (DOE) analysis over the different input variables revealed that the average gas production rate was a strong function of eight features, including well geometry, completion interval, and completion design parameters. Furthermore, a one-hidden layer neural network model containing 14 neurons with Bayesian regularization (br) algorithm and tan-sigmoid and linear activation functions provided the best predictions.
In 2018, the contribution of the oil and gas sector to Canada’s GDP increased to 5.6% (NRCan, 2020). In the province of Alberta and Saskatchewan, this percentage was equal to about 30% and 23%, respectively. Hence, while the health of the oil and gas sector is crucial to the whole Canadian economy, it plays a dominant role in the economics of Alberta and Saskatchewan (Globerman & Emes, 2019). The oil and gas sector in Canada is divided into three main segments: upstream, midstream, and downstream. The upstream section deals with the exploration and production of crude oil and natural gas.
ABSTRACT Advancements in the design of sonic logging tools have made it possible to characterize rock formations more extensively. This achievement has had a great impact on the design of effective drilling, completion and production practices. However, interpretation of the data acquired by advanced sonic logging tools is complex, and the relative contributions of intrinsic and stress-induced elastic property anisotropy on tool response are not well understood. This research presents a workflow for predicting sonic logging tool response accounting for the effects of bedding and drilling-induced stresses, based on anisotropic and stress-dependent dynamic and static elastic properties of shale samples from the Montney Formation. Shear wave slowness values obtained from simulations compare favourably with values based on experimental results, and the character of dispersion curves for the simulation outputs is mostly consistent with expectations. Comparison between experimental results and real field data shows notable differences in the absolute values of slowness. These differences are believed to result from differences between lab testing conditions and in-situ conditions such as temperature, frequency, size (dimensions), pore fluid properties, pore pressure, and rock property heterogeneity. 1. INTRODUCTION A new generation of specialized sonic logging tools has provided the capability of obtaining a multitude of formation properties more accurately, including acoustic anisotropy, in-situ stress magnitudes and directions, permeability, and pore fluid type. These properties assist in the design of effective drilling, completion and production operations. The factors affecting a tool's response can be quite complex. Among these factors, the most relevant to this research are bedding related and stress-induced anisotropy. This research was aimed at developing a methodology for predicting sonic logging tool response accounting for intrinsic and stress-induced anisotropy of both static and dynamic elastic properties. To achieve this objective, laboratory testing results obtained on rock samples the from Montney Formation were used in conjunction with a newly-developed numerical modelling workflow to simulate stress states around vertical and horizontal boreholes in the Montney Formation, and to predict sonic logging results.
Vaisblat, Noga (University of Alberta) | Rangriz Shokri, Alireza (University of Alberta) | Ayranci, Korhan (University of Alberta) | Harris, Nick (University of Alberta) | Chalaturnyk, Rick J. (University of Alberta)
Abstract This paper presents a critical insight into evaluation of elastic properties of the Montney Formation siltstone through indentation measurements and log-derived elastic moduli, including Young’s modulus, Poisson’s ratio, and brittleness. Further, we explored the relationship between geomechanical properties and rock fabric, mineralogy, and its role in hydraulic fracturing treatments. We examined seven wells along a northwest-southeast cross-section, sub-parallel to basin dip. Facies analysis was conducted on four long Montney cores (70 to 250 m). Young’s modulus, Poisson’s ratio, and brittleness were calculated from dipole sonic and density logs. Where shear sonic log was not available, predictions of shear wave velocity were performed from near-by wells. Hardness profiles of core samples, measured by a hand-held indentation device, were compared with rock composition from QEMSCAN (mineralogy) and LECO-TOC (organic matter). A coupled hydro-mechanical code, capable of explicit inclusion of lithofacies variation and bedding discontinuities, was employed to investigate the response of the siltstone to hydraulic fracture propagation in the Montney formation. A comprehensive facies analysis revealed 16 lithofacies across the basin, with depositional environments ranging from tidal flat to offshore sediments, and deep-water turbidite deposits. The variations of Young’s modulus, Poisson’s ratio, and relative brittleness from well logs were compared against indentation measurements of the four long cores and against rock composition in all wells. Young’s modulus, brittleness, and hardness showed similar trends in each well, while Poisson’s ratio demonstrated a trend with depth opposite to all other elastic parameters. No clear distinction was found between the geomechanical properties of different lithofacies in each well. More importantly, similar lithofacies commonly exhibit significantly different geomechanical properties in different wells. The analysis from coupled numerical simulations also confirmed that effective fracture propagation was not necessarily lithology controlled; rather it was greatly constrained by geomechanical contrasts. Further statistical analysis indicated that clay content, and to a lesser extent organic matter content, had the strongest control on elastic moduli in the Montney Formation, reducing Young’s modulus, brittleness, and hardness, but increasing Poisson’s ratio. Our study concludes that unlike other unconventional reservoirs, geomechanical properties in the Montney Formation are not lithofacies-dependant. We attribute the weak influence of depositional environments on the sediment to the size and compositional homogeneity of detrital material that entered the basin. Clay minerals and organic matter were identified as controlling factors on elastic moduli -and thus hydraulic fracture propagation- in the Montney Formation.
Abstract Diagnostic fracture injection tests (DFIT's), or "mini-fracs" are often used to gauge many reservoir and fracture design parameters. However, DFITs are not always conducted in conjunction with the main completions work. This paper proposes a novel workflow to determine the instantaneous shut-in pressure (ISIP) from readily available completions data. This is a valuable parameter in itself as related to the least principal in-situ stress states as demonstrated by the stress change relationships near faults in Lavoie et al. (2018). Directly using completions data from fracture stimulation operations, the authors have leveraged on the water-hammer signature in bottom-hole pressure data during completions to process the ISIP for each completions stage. Within this study, completions data from ~2100 stages from ~300 horizontal Montney formation wells were analyzed. A MATLAB script was used to automate the derived ISIP stress trends over the Montney formation and to deduce the ISIP in a consistent format. This novel workflow also validates the expected in-situ stress trends at depth, with a relationship of high ISIP gradients closer to fault zones similar to stress change behaviour as shown in Lavoie et al. (2018). Specifically, a positive spatial relationship was observed pertaining to local ISIP gradients, the lithostatic gradient, the minimum in-situ stress, and the propagation of hydraulic fractures that are prone to reactivation of critically stressed faults. Based on our real-time observations, field operators may allow to flowback a well for a short amount of time to deplete the anthropogenic reservoir pressure and stress shadowing prior to resuming fracture stimulation. Considering the continued push for higher fluid and sand loading in industry in the development of unconventional assets as an economic driver, there also exists a large and tangible corporate citizenship opportunity of mining real time completions dark data with the possibility of relating that live feed as a prescriptive tool to mitigate reactivation of critically stressed faults. This case study focuses on the Montney formation as a basis for processing easily available data from standard operations in an effort of systematically designating areas prone to seismicity risk in future hydraulic fracturing operations based on automated real-time analytics of dark data.
Abstract Hydrodynamics and geothermics are important tools for understanding the complex distribution of reservoir fluids in the Montney Formation in Alberta and British Columbia, Canada. The Montney comprises a conventional system in the east and an unconventional, Deep Basin-style hydrocarbon system in the west, where an underpressured, oil-dominated fairway just west and downdip of the conventional system grades further downdip into overpressured liquids and gas fairways. The first part of this study addresses how these systems can be mapped from a pressure and temperature perspective. The Montney hydrodynamics system is explained using pressure versus elevation graphs. Key contours are taken from pressure-depth ratio maps to define the general boundaries between systems, noting that these boundaries change with depth. Geothermal gradient mapping is used to identify areas of prominent high or low geothermal gradients, which can have a significant effect on the positioning of gas liquids fairways. Key current day isotherms are also identified to represent the current phase windows by relating present-day formation temperatures to Tmax data. To evaluate how pressure and temperature affect liquids production within the Montney, liquids production trends need to be considered. The second half of the paper discusses how mapping gas composition, particularly C2+ Wet Gas Index (WGI), may serve as a good proxy for liquids yields. While the authors appreciate the complexities of phase behavior and the various factors influencing liquids production, the objective of this paper is to link trends that can be observed in liquids production to trends in pressure, temperature and gas composition. Ultimately, this paper examines ways in which hydrodynamics and geothermics can be used to help predict spatial variations in observed liquids production. By analyzing the co-relationships of the pressure, temperature and WGI data, the Montney segregates into two distinct domains which we term the Northern (British Columbia) Play and the Southern (Alberta) Play. This analysis can be tied in with other data sets for a better understanding of the reservoir such as: isotope geochemistry to gain insights into hydrocarbon migration; Special Core Analysis (SCAL) data to gain insights into fluid mobility; vapour-liquid equilibrium data to examine hydrocarbon fractionation during production; and completions data to provide a more complete picture of reservoir deliverability.
Euzen, Tristan (IFP Technologies (Canada) Inc.) | Watson, Neil (Enlighten Geoscience Ltd.) | Chatellier, Jean-Yves (Tecto-Sedi Integrated Inc.) | Mort, Andy (Geological Survey of Canada) | Mangenot, Xavier (Caltech)
Abstract With the development of unconventional resources, the large number and high density of well data in the deep/distal part of sedimentary basins offer new avenues for petroleum system analysis. Gas geochemistry is a widespread and inexpensive data that can provide invaluable information to better understand unconventional plays. This paper illustrates the use of early production gas composition as a proxy for in-situ hydrocarbon phase distribution in the Montney play of westernmost Alberta and northeastern British Colombia. We demonstrate that a careful stratigraphic allocation of the landing zone of horizontal wells is a key step to a meaningful interpretation and mapping of gas geochemical data. The regional mapping of the dryness of early production gas from the Montney formation clearly delineate thermal maturity windows that are consistent with available carbon isotopic data from produced and mud gas. Integrating this mapping with pressure and temperature data also highlights gas migration fairways that are likely influenced by major structural elements and compartmentalization of the basin. In the wet gas window, reported condensate-gas ratios show that the liquid recovery from multi-stage fractured horizontal wells is highly variable and strongly influenced by variations in reservoir quality and stimulation design. Understanding in-situ fluid distribution can help narrow down the number of variables and identifying key controls on liquid recovery. Several examples combining produced and mud gas data illustrate the use of geochemistry to better constrain geological and operational controls on productivity and liquids recovery in the Montney play. Introduction With the rapid development of unconventional resources, a wealth of new data has been released from historically undrilled or poorly documented portions of sedimentary basins. The large number and high density of well data over extended areas of deep/distal parts of these basins offer invaluable information and new perspectives for petroleum system analysis. In the Montney play of Western Canada, the distal unconventional part of the basin covers an area of approximately 65,000 square kilometers and has been penetrated by over 7,000 horizontal wells. Due to sustained low gas price in North America over the past decade, most of the industry activity has been focused on the liquids-rich gas and light oil fairways of this resource play. Production data show that although a broad liquids-rich fairway can be defined at the basin scale, local variations of fluid distribution and reservoir quality strongly affect the liquid recovery from horizontal wells. The geochemical compositions of both produced gas and mud gas provide a powerful tool to investigate those variations, their geological controls and their impact on well performance. While this paper focuses on the fluid distribution, numerous studies have documented the influence of reservoir quality on the liquid recovery in the Montney play (Chatellier and Perez, 2016; Kato et al., 2018; Akihisia et al., 2018; Iwuoha et al., 2018).