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Abstract Maximizing recoveries and understanding associated reserve uplifts through the optimal completion design are major themes across North American unconventional plays. Thus, recognizing where the Montney sits in context to other plays in Western Canada is an integral component in evaluating the competitive landscape for investors and operators. This analysis compares completion trends across North America to highlight the impact of different completion designs on well performance. Additionally, the effect of completion designs on parent-child communication is analyzed. An overview of the industry regarding proppant intensity (lbs/ft) and the lateral length is presented, followed by an in-depth statistical analysis on the Montney and Deep Basin plays in the Western Canadian Sedimentary Basin (WSCB). The parent-child well analysis highlights how operators in the Montney Formation can maximize resource recovery while mitigating well failure. The study finds proppant intensity is the driving force behind the rate of change in the industry. However, larger completions can increase associated risk with offsetting well failures as plays move further into development mode. Our results suggest operators can maximize resource recovery through increased completion intensities and minimize parent well failure by moving toward pad completions. Introduction The Montney Formation, a Triassic siltstone that contains multiple stacked zones, is one of the most actively drilled plays in Canada. With the upstream oil and gas industry becoming increasingly competitive, it is imperative to understand where the Montney sits among its peers. Analyzing trends across North America, proppant intensity levels increased in recent years in many of the key resource plays in the U.S. However, operators in Canada tend to be more conservative and have yet to adopt similar completion designs. This study analyzes completion trends across North America and focuses on parameters such as proppant intensity and lateral length. A statistical evaluation of completions in the Montney and Deep Basin plays shows the effect of each parameter on EURs, and the parent-child analysis on the Montney Formation examines the risk associated with frac'ing offsetting child wells.
Chesapeake Energy is partnering with RS Energy Group to improve operational efficiency and capital discipline by employing advanced analytics and machine learning. RS Energy is a Calgary-based energy research firm founded in 1998 covering more than 150 operators in the major North American and international oil and gas regions, including the US shale plays. It provides technical analysis of basins, including completions and production, as well as asset evaluations for operators considering acreage additions. All of this is done within the context of shifting capital markets. Chesapeake announced the pact fresh off its $4-billion merger with WildHorse Resource Development, which bolstered its position in the Eagle Ford Shale of South Texas.
Abstract What has been regarded as unconventional by many has been conventional for the few that have developed viable, and in many cases, very profitable projects that succeeded in extending the envelope of recoverable hydrocarbons. The range of recoverable hydrocarbons is bounded by physical constraints like reservoir properties, unfavorable hydrocarbon characteristics, or in many cases, hydrocarbons still captured in the formations. The methods of extraction that have been devised to address such challenges frequently combine applications of enhanced recovery, intensive stimulation, or advanced drilling. The distinction between conventional and unconventional can be reasoned not in the context of new versus old, more or less prolific accumulation attributes, or the method of extraction, but in the approach that is followed. A conventional development approach would cover the entire field with increasing intensity; an unconventional approach would pursue intensive development sequentially from the more prolific to the less prolific parts of the field. Although it is easy to see the technical benefits of unconventional methods, their integration with economic and market parameters is necessary in order to provide a successful implementation. The purpose of this paper is to present a set of features that are found as common denominators in successful developments of such resources. These common features could be viewed as unconventional resource development principals that outline the areas where particular attention is required for their ultimate success. In that context, the influence of market pricing, cost structure and resource evaluation are examined and offer a platform for project implementation that can be viewed as a "road map". The basic premise in this reasoning is that while it is not possible to develop a universal technological solution, careful adaptation of technology to the subsurface and operational realities allow synergies to develop that improve the chance of economic success. The recognition of key risks in unconventional resource development is instrumental in addressing the reasoning in resource classification. In that sense, while geological risks are often viewed as key in conventional resources, unconventional resources carry more risks as more complex extraction methods are implemented. Once development commences, it could maintain a positive outlook with changing market conditions as the associated costs relate to some extent to fuel costs that are in turn dependent on the product prices. The long development cycle of such projects lasting more than 20 years reduces the exposure to fluctuating prices as long as a reasonable average trend is considered at the onset. With evolving methods of extraction, the oil industry is shifting focus from exploration into intensive exploitation. Inevitably, the terms unconventional resources and a non conventional approach present a new direction for realizing the fuel sources of the future.
Abstract Recent wells from the Haynesville Shale show materially higher productivity and expected recoveries compared to older vintages. The improvements, visible on absolute and lateral-normalized bases, occurred in both the historical core of the play and previously uneconomic areas, with well costs decreasing even as completion designs intensified. This study analyzed geologic, completion, and production data across the Haynesville play in a multivariate analysis to characterize and identify the key drivers making this play successful and expanding its economic limits. Introduction The Haynesville Shale of East Texas and North Louisiana was viewed as one of the most prolific shale-gas resources when it first emerged in late 2008. Production quickly increased into 2011 as it became the largest- producing gas play in the Lower 48, a reflection of highly productive wells and favorable commodity prices. The crash of natural gas prices in 2012, a surge of Appalachian gas production and the Haynesville's steep declines and high well costs shifted activity and interest away from the play. In 2015, despite weak commodity prices, rig activity rebounded as operators began to drill more completion-intensive wells with considerable well cost reductions. Recent results show significant improvements in both the historical core of the play and previously uneconomic areas. This study analyzed geologic, production and completion data to characterize the Haynesville play to identify key drivers making this play successful. The Upper Jurassic Haynesville Shale is an organic- and carbonate-rich mudrock deposited in a restricted basin, with paleo structures and topography strongly influencing lithology trends across the play (Steinhoff et al, 2011; Hammes et al, 2011). The Haynesville reservoir ranges from 100– to 300-feet thick and is characterized by overpressuring (pressure gradients of 0.8 to 0.9 psi/ft), 8% to 15% porosity and depths of 11,000 to 14,000 feet. The play currently produces about 5 Bcf/d of dry gas, with initial peak calendar-day rates from recent wells averaging 15 MMcf/d. These new wells are forecast to recover about 10.5 Bcf over 30-year producing lives.
Abstract One of the major challenges associated with the exploitation of unconventional hydrocarbon resources is determining the optimal stimulation design. In this sense, it is necessary to understand how the parameters and variables involved in the completion process impact on production performance; the purpose is to act on such controllable variables and, consequently, maximize production and field development efficiency. Whereas physical driven tools frequently used in the oil industry are very helpful, they always imply a set of assumptions and simplifications regarding the system or phenomenon they try to model; they also require a large amount of unavailable or expensive data to calibrate them. Generally, different combinations of model parameters could explain well production behavior and for each of these solutions the way to optimize completion and development may be different. Because of these drawbacks, and the big number of unconventional wells available, data-driven workflows have gained popularity in the last years. These models represent an excellent complement to physical driven tools in the attempt to optimize the completion and development strategy in shale plays. Several publications used both parametrical and non-parametrical models in the search of the Holy Grail: a statistical model capable of predicting how stimulation design affects productivity. The aim of this paper is to develop a novel methodology to understand the relation between formation parameters, completion design variables and production performance. An artificial neural network model (ANN) was chosen for this study. Public production and stimulation data was merged with geological and petrophysical properties maps for almost 13.000 horizontal wells landed in Eagle Ford formation. A back propagation ANN algorithm was trained with this data-set and a cross-validation criterion was used for hyper-parameters optimization. Once the optimal model was selected, a bootstrap algorithm was run to assess for uncertainty in model prediction; these models were trained to determine which part of the input space presented enough data to get a clear signal and in which part the amount of data was not enough to differentiate signal from noise. ANN models proved to be a fine method for this purpose obtaining R-Squared values between 0.5 and 0.7 for cross-validation sets. Significant relations were observed between production performance and lateral length, true vertical depth, porosity and fracture fluid intensity. The methodology presented in this paper introduces a novel feature in comparison to previous publications regarding model uncertainty assessment. The coupling of the ANN model with the bootstrap re-sampling technique allowed to better understand which conclusions were statistically significant and which not, a fact that proved to be vital to correctly interpret results. It was demonstrated that such methodology is a good complement to physical-driven models in the aim to comprehend the relation between formation parameters, completion design variables and production performance.