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Sochovka, Jon (Liberty Oilfield Services) | George, Kyle (Liberty Oilfield Services) | Melcher, Howard (Liberty Oilfield Services) | Mayerhofer, Mike (Liberty Oilfield Services) | Weijers, Leen (Liberty Oilfield Services) | Poppel, Ben (Liberty Oilfield Services) | Siegel, Joel (Liberty Oilfield Services)
Abstract The shale industry has changed beyond recognition over the last decade and is once again in rapid transition. While we are unsure about the nature of innovations to make US shale ever more competitive, we are certain that the current downturn will drive a further reduction in $/BO – the total cost to lift a barrel of US shale oil to the surface. As a result of an increase in scale and industry efficiency gains, the all-in price charged by service companies to place a pound of proppant downhole has come down from more than $0.50/lb in 2012 to about $0.10/lb today. In this paper, we discuss what components have contributed to this reduction to date and use several case studies to illustrate the potential for further cost reductions. The authors used FracFocus data to study a variety of placement and production chemicals for about 100,000 horizontal wells in US liquid rich basins, including the Williston, Powder River, DJ, Permian basins, as well as SCOOP/STACK and Eagle Ford. All chemicals used were averaged on a per-well basis into a gallon-per-thousand gallons (gpt) metric. In the paper, we first provide an overview of trends by basin since 2010 for these chemical additives. Then, we perform Multi-Variate Analysis (MVA) to determine if groups of these chemicals show an impact on production performance in specific basins or formations. Finally, through integration of lab testing (on fluid systems and proppants), a liquid-rich shale production database and FracFocus tracking of industry trends, the authors developed a list of case histories that show modest to significant reductions in $/BO. In this paper we focus on proppant delivery cost – the cost to place a pound of proppant in a fracture downhole, where it can contribute to a well's production for years to come. The last decade saw a 10-fold increase in horsepower, a 20-fold increase in yearly stages pumped and a 40-fold yearly proppant mass increase. One result of this increase in scale, was a gain in efficiencies, which led to an average 3-fold fracturing cost decrease to place a pound of proppant downhole. We will document this trend in detail in the paper. A significant industry trend over the last decade has been a "viscosity for velocity" trade. The change to smaller mesh regional proppants, in combination with an increase in pump rates on frac jobs in the US, has allowed fluid systems to become more "watery". At the same time, the industry is moving from guar systems to polyacrylamide-based systems that exhibit higher apparent viscosities at low to ultra-low shear rates. These newer High Viscosity Friction Reducer (HVFR) systems show superior proppant carrying capacity over traditional slickwater fluid systems. Regained conductivity testing has shown that these HVFR systems are generally cleaner for fracture conductivity than guar systems. Along with changes to base chemistry, a 2- to 5-fold increase in disposal costs and an overall "green initiative" over the last decade have resulted in a push to maximize recycled water usage on these HVFR jobs. These waters can be in excess of 150,000 TDS (Total Dissolved Solids) which present challenges across the board when designing a compatible fluid system that fits the needs in terms of viscosity yield, scale inhibition and microbial mitigation etc. – all while keeping costs low. Specialty chemicals, such as Hydrochloric Acid (HCl) substitutes that have similar efficacy as HCl but significantly lower reactivity with human skin, have helped significantly to improve operational safety around previously-categorized hazardous chemicals, and have helped reduce cost and improve pump time efficiency. Measurement of bacterial activity during and after fracture treatments can help with the best economic selection of the appropriate biocide. These simple measurements can help further reduce what is spent on the necessary chemical package to effectively treat a well. This paper provides a holistic view of fluid selection issues and shows a real-data focused methodology to further support a leaner approach to hydraulic fracturing.
Liu, Chuxi (The University of Texas at Austin) | Yu, Wei (Sim Tech LLC.) | Chang, Cheng (The University of Texas at Austin) | Li, Qiwei (Petrochina Southwest Oil&Gas Field Company) | Sepehrnoori, Kamy (The University of Texas at Austin)
A robust and reliable workflow for well spacing optimization in shale reservoirs development incorporating various types of uncertainties and detailed economics analysis is paramount to achieve a sustainable unconventional production. In this study, we show a novel well spacing optimization workflow based on the results of assisted history matching and apply it to a real shale gas well, incorporating uncertainty parameters such as matrix permeability, matrix porosity, fracture half-length, fracture height, fracture width, fracture conductivity and fracture water saturation. The input ranges of these parameters are 10 nd to 1000 nd, 0.038 to 0.083, 200 ft to 780 ft, 25 ft to 65 ft, 0.1 ft to 4 ft, 10 md-ft to 200 md-ft, and 0.5 to 0.9 respectively and are determined from field experience and exisiting information. Results from assisted history matching are gathered with a total of 60 HM (history matching) solutions out of 325 runs that meets the criteria of BHP (bottomhole pressure) error less than 25% and WGR (water gas ratio) error less than 60%. A total of 1548 proxy solutions out of 100,000 samplings are obtained from MCMC (markov chain monte carlo) algorithm. Embedded discrete fracture model (EDFM) is used to model hydraulic fractures along with a commercial reservoir simulator. The use of greatly facilitates the modelling process of hydraulic fractures compared to the LGR (local grid refinement) method. The uncertainty parameter distributions from our workflow is matching the posterior distribution obtained from assisted history matching. It is found out that the optimal well spacing is approximately 885 ft, with an estimated net present value (NPV) of 6.67 million dollars. Economic uncertainty evaluation is performed and it is discovered that the NPV distribution obtained from the history matching solution is more concave than the results obtained from KNN (k-nearest neighbor) proxy prediction. The optimal well spacing of 885 ft obtained from this workflow is matching closely with the field experience of approximately 1000 ft. The P50 of the NPV distributions of five spacing (1550 ft, 1033 ft, 775 ft, 620 ft, and 517 ft) are 4.04, 5.91, 7.35, 6.42, and 5.44 million dollars respectively. Gas estimated ultimate recovery per well (P50) for the abovementioned spacings are 3070, 3020, 2945, 2750, and 2565 million cubic feet respectively. There is a drastic drop of gas estimated ultimate recovery per well about 6.6% going from a spacing of 775 ft to 620 ft, indicating the onset of well interference between these distances’ range. The practicality and the convenience of our workflow make it possible to be applied to any shale gas well.
Production history match can be used to evaluate effective fracture geometry and to confine the uncertainty of fracture and reservoir properties such as fracture conductivity and relative permeability. Although these parameters are critical in optimizing completions design such as well and cluster spacing, they are unfortunately difficult to be quantified using fracture modeling or most diagnostic techniques, which focus on geometry and properties during fracturing, different from those during production.
To tackle this challenge, we leveraged the automatic history match (AHM) scheme based on Neural Network-Markov Chain Monte Carlo (NN-MCMC) to compare the parameters of a horizontal shale gas well with 74 days production history. 10 parameters characterizing the fracture and reservoir properties were quantified. The case with and without enhanced permeability area (EPA) were investigated. The posterior distributions of these parameters were obtained from the multiple history matching solutions. These multiple solutions were found by probabilistically iterating through 1 million realizations using the NN-MCMC algorithm and a total of 650 realizations were proposed to be validated with reservoir simulator. The MCMC algorithm has the advantage of quantifying uncertainty without bias or being trapped in local minima. The employment of neural network (NN) as a proxy model unlocks the limitation of an infeasible number of simulation runs required by a traditional MCMC algorithm. The proposed AHM workflow also utilized the benefits of Embedded Discrete Fracture Model (EDFM) to model fractures with a higher computational efficiency than a traditional local grid refinement (LGR) method and more accuracy than the continuum approach.
We found that when EPA was included to represent small fractures surrounding main hydraulic fractures, the shorter fracture geometry posterior distributions were obtained compared with the case of hydraulic fractures only (without EPA). This causes the production forecast of the case with EPA to be significantly lower than the one with only hydraulic fractures (without EPA). This means that if a simple model with only hydraulic fractures was assumed while in the actual operation, there is EPA due to the small fracture networks created around main hydraulic fractures, we would overpredict the fracture geometry and gas EUR prediction. With the use of NN-MCMC as history matching workflow, the uncertainty range of 10 parameters were characterized automatically. These effective fracture geometry and properties can be used to improve well spacing and completion design in the next fracturing campaign.
Abstract Completion design is widely considered as a dynamic area of experimentation and development due to its significant impact on production performance in unconventional reservoirs. As the industry moves beyond the paradigm of proppant and base fluid analysis, more and more focus is being given to the composition of chemicals being used in the completion design. This paper focuses on quantifying the true impact of these chemicals used in completion design by using machine learning to solve this multivariable problem and creates value by providing a framework to help completion engineers select the optimum chemicals and aid in fracture treatment design process, using a data-driven approach. Depending on the shale formation being fractured, a typical fracture treatment job uses a very low concentration of 3-12 additive chemicals. This selection is carried out from thousands of commercially available chemicals and is based on treatment job performance carried out during the modeling phase as well as from field experience. In this work the authors have tried to deconstruct the varied chemical groups used in a hydraulic fracturing job, to analyze its impact independently. In order to understand the impact of hydraulic fracturing chemicals on short term (6 month BOE), midterm (12 month BOE) and long term (24 months BOE) productivity, an analysis is performed using feature selection methods like SelectKBest (F Regression score), Tree-based regression, Mutual Info Regression, Recursive Feature Elimination (RFE) and Correlation-based Feature Selection (CFS). In this paper, Max Ingredient Mass (LBS) is utilized for correlation with production to determine if more or fewer quantities should be used in the Powder River Basin. Comprehensive data exploration is carried out to generate correlations between the chemicals and productivity which helps to identify and link multiple causes to effects and weigh the strength of the correlations, which might not be obvious to the naked eye. A comparison of algorithms for three different target variables shows how certain chemicals are significant at the beginning of production and gain/lose their importance with time. The present paper provides in-depth insight into the impact of the most commonly used chemical types in shale completions. It uses machine learning to provide a truly novel understanding of the individual contribution of these products. It adds to the understanding around this concept by developing a cause and effect relationship between chemical design and composition and the impact on production and provides recommendations on process optimization in completion design.
Travers, Patrick (Dolan Integration Group) | Burke, Ben (HighPoint Resources) | Rowe, Aryn (HighPoint Resources) | Hodgetts, Stephen (Dolan Integration Group) | Dolan, Michael (Dolan Integration Group)
Abstract Scope: The management, treatment and disposal of hydraulic fracturing flowback fluids and produced water presents a major challenge to operators. Though the volumes of water are tracked closely during operations, the sources of that water are not well understood. The objective of this study is to apply a cost effective and proven technique, stable isotope analysis, along with an extensive sampling program (n>1,500 samples) to describe the contributions of variable water sources through completions, flowback and the production lifecycle of multiple horizontal, hydraulically fractured wells in the Denver Basin, Colorado. Methods: The water stable isotopes of hydrogen (H and H) and oxygen (O and O) are conservative tracers and particularly advantageous because they occur naturally in these systems and rely on well-established scientific and analytical techniques. Sample collection is simple and does not require specialized equipment or operational downtime. 80 horizontal, hydraulically fractured wells completed in the Cretaceous Niobrara or Codell Formations were selected for this study. More than 1,500 samples were collected and analyzed in total, including: baseline samples of the source water used to stimulate the well, time series samples collected at daily or semi-daily intervals during the early weeks of flowback, and samples collected several months after the wells were brought on production. Samples of produced water were also collected from legacy wells in the field as well as offset wells being monitored for frac hits during completions. Results: Samples of the near surface and shallow aquifer source water collected prior to hydraulic fracturing fell on or near the global meteoric water line (GMWL) as defined by Craig (1961). This isotopic signature is expected for modern water in aquifers charged by precipitation. In contrast, samples collected during flowback and production were significantly enriched in H and O. Furthermore, the magnitude of the isotopic difference between the source and flowback water increased with time until equilibrating after several months. This equilibrated composition is consistent for Niobrara and Codell wells in the field, as well as legacy wells sampled and consequently is hypothesized to be indicative of native formation water. The study did find exceptions, particularly with wells known to be connected to major fault or fracture networks. These samples deviated from typical formation water signatures, potentially indicating the migration of deeper sourced fluids or the vertical mixing of shallower fluids with Cretaceous waters. Significance: The scale of this study is unique in the literature and provides novel and comprehensive insight into the dynamics of flowback and the sources of produced water in the Denver Basin. This study demonstrates that these data can clearly differentiate water injected during stimulation from native formation waters, as well as track the magnitude and duration of well cleanup. It can also identify wells that may be producing water with a unique composition due to fluid migration through faults or fracture networks or due to nearby well communication.
Rosenhagen, Nicolas M. (Colorado School of Mines) | Nash, Steven D. (Anadarko Petroleum Corporation) | Dobbs, Walter C. (Anadarko Petroleum Corporation) | Tanner, Kevin V. (Anadarko Petroleum Corporation)
Abstract The volume of stimulation fluid injected during hydraulic fracturing is a key performance driver in the horizontal development of the Niobrara formation in the Denver-Julesburg (DJ) Basin, Colorado. Oil production per well generally increases with stimulation fluid volume. Often, operators normalize both production and fluid volume based on stimulated lateral length and investigate relationships using "per-ft" variables. However, data from well-based approaches commonly display such wide distributions that no useful relationships can be inferred. To improve data correlations, multivariate analysis normalizes for parameters such as thermal maturity, depth, depletion, proppant intensity, drawdown, geology and completion design. Although advancements in computing power have decreased cycle times for multivariate analysis, preparing a clean dataset for thousands of wells remains challenging. A proposed analytical method using publicly available data allows interpreters to see through the noise and find informative correlations. Using a data set of over 5000 wells, we aggregate cumulative oil production and stimulation fluid volumes to a per-section basis then normalize by hydrocarbon pore volume (HCPV) per section. Dimensionless section-level Cumulative Oil versus Stimulation Fluid Plots ("Normalization" or "N-Plot") present data distributions sufficiently well-defined to provide an interpretation and design basis of well spacing and stimulation fluid volumes for multi-well development. When coupled with geologic characterization, the trends guide further refinement of development optimization and well performance predictions. Two example applications using the N-Plot are introduced. The first involves construction of predictive production models and associated evaluation of alternative development scenarios with different combinations of well spacing and completion fluid intensity. The second involves "just-in-time" modification of fluid intensity for drilled but uncompleted wells (DUC's) to optimize cost-forward project economics in an evolving commodity price environment.
Hazra, Suchandra (Dynachem Research Center) | Madrid, Vanessa (Dynachem Research Center) | Luzan, Tatiana (Dynachem Research Center) | Van Domelen, Mark (Downhole Chemical Solutions) | Copeland, Chase (Downhole Chemical Solutions)
Abstract This paper provides a detailed evaluation of the impact that field source water chemistry has on the performance of friction reducers being used for hydraulic fracturing. In this research, correlations are established between friction reducer performance and source water chemical composition, allowing operators to shorten the learning curve within their fracturing operations, use the most appropriate fluid systems, and potentially mitigate job failures. Extensive testing has been conducted to evaluate friction reducer performance in the presence of different ionic components such as calcium, magnesium, iron and chloride. Performance testing was determined by varying individual ions, as well as using source waters from multiple field locations having total dissolved solid (TDS) levels of well over 100,000 ppm. Testing parameters included friction reduction, hydration rate via viscosity, and rheological characterization for viscosifying-type friction reducers. Principal component analysis was used as statistical tool to characterize the variation in water chemistry and to establish its relationship with friction reducer performance.
ABSTRACT: This paper targets a comprehensive predictive model to evaluate the key success of completion strategies (treatment) for major successful shale plays and guide future selective optimum completion for each shale play. Many important parameters that control producing well behaviors such as number of horizontal wells, spacing between fractures and wells, horizontal well completion configurations, stages per well, fracture type, average water requirement, depth, proppant type, hydraulic horsepower(HHP) per stage, Lb/ft2 of proppants per stage, number of stages, and lateral length of the horizontal wells, have been analyzed.
The proposed analysis is performed on 12 major shale gas and oil plays, for which the data were available. The analysis of the data identified similarity in completion strategies. Learning from these analyses can be used to predict completion strategies in new wells of old or new shale plays.
A case study from Niobrara shale (Colorado) is investigated. The procedure used in exploring the case study can be used as a decision criterion for similar cases in deciding stimulation configurations and main important factors that lead to the optimum way of developing these resources. Principal component analysis (PCA) is used to correlate the commonly used completions strategies with geochemical and geomechanical properties of shale rocks.
Many horizontal wells and fracture stages are needed to drain a shale reservoir, and these is a need for effective fractures and horizontal wells to produce these reservoirs. We conducted a case study of the use of data analytics to study the Niobrara shale rock, and suggest the best completion strategies used to effectively produce from the shale rock.
We then compared the characteristics of the Niobrara shale rock with the 12 most common shale plays in North America. Geochemical and geomechanical properties of these shale rocks vary significantly among the reservoirs and vary within the same reservoir. We believe that this paper will assist in selecting proper completion techniques for horizontal wells.
Summary Unconventional reservoirs are multivariate problems requiring integration of data across multiple disciplines. Microseismic data recording hydraulic fracture treatments in the Niobrara Formation in the Denver Basin are no exception. Microseismic data are the real time recording of the subsurface reaction to stimulation and the stimulation is affected by the lithology and structure penetrated by the wellbore. Microseismic data are often not studied at the wellbore scale, yet this is where the stimulation initiates. Integrating both cluster analysis and horizontal borehole imagery can aid in the interpretation of microseismic data. Due to the complex stratigraphy and structure in the Denver Basin, horizontal wells rarely target a single lithology. The varying lithology targeted by the wellbore could lead to stimulation heterogeneity and recorded via microseismic data. Vertical well cluster analysis was applied to one horizontal well to quantify the number of stage locations per lithology and how microseismic magnitude is affected by the lithology at each stage location. The number of natural fractures in each lithology was also quantified. In addition, horizontal borehole imagery identified natural and drilling induced fractures which aided in the interpretation of microseismic data heterogeneity. These interpretations can help understand how lithology and structure control stimulation heterogeneity and thus recorded by microseismic data. Microseismic magnitude was found to be 43% higher in stage locations within higher Young's Modulus (chalk) rock compared to stage locations within lower Young's Modulus (marl) rock. In addition, chalk was found to be more naturally fractured than marl, although the marl still contains natural fractures. Finally, image log analysis showed linear microseismic trends are due to lack of natural fractures and are affected by and parallel maximum horizontal stress (σH) whereas clustered microseismic trends are due to abundant, conjugate natural fractures. Introduction The research presented is a part of a joint research effort between the Reservoir Characterization Project and Anadarko Petroleum Corporation. The Wattenberg Project, Phase XV, began July 1, 2013 with the main objective of the Wattenberg Project is to guide well spacing and completions to improve ultimate hydrocarbon recovery. Unconventional reservoir development is dependent upon integration of data and multiple disciplines to solve multivariate problems. This research focuses on integrating both vertically derived, horizontally applied cluster analysis and horizontal borehole imagery to identify how geological heterogeneity influences completions and thus microseismic data recording the stimulation along the wellbore.
Miller, Fred (Carrizo Oil & Gas, Now with Navigation Petroleum) | Payne, Jon (Eureka Geological Consulting, Formerly with Liberty Resources) | Melcher, Howard (Liberty Oilfield Services) | Reagan, Jim (Liberty Oilfield Services) | Weijers, Leen (Liberty Oilfield Services)
Abstract The Denver-Julesburg (DJ) Basin has seen oil and gas production for more than a century. It is going through a new cycle of development with horizontal drilling and high-intensity hydraulic fracturing. Since the first horizontal wells in 2008 nearly 4,000 Niobrara and Codell horizontals have been drilled. While completion practices have remained fairly standard across the basin, production results vary wildly. We utilized a high-quality digital log dataset to accurately characterize reservoir quality in the Niobrara and Codell formations in the DJ Basin. The final dataset included 562 digital logs spread across the current extent of horizontal drilling in the DJ Basin. A petrophysical workflow was developed and detailed mapping of the reservoir attributes was completed. The log derived parameters, along with an aeromagnetic and vitrinite reflectance dataset, provided excellent insight into which geologic parameters could be best tied to well production response. Through bivariate and multivariate analyses using reservoir and completion data, and an economic evaluation to determine the "best bang for your buck", we have identified several completion changes for the basin that result in a significant reduction in the cost per bbl of oil produced. While geological parameters have been found to matter greatly for the production success of DJ horizontals, completions matter as well. The high GOR areas of Inner Core Wattenberg benefit most from jobs with more proppant, whereas areas with poorer reservoir quality generally benefit from higher stage intensity and jobs with larger fluid volumes. All suggested completion changes have a major impact on lowering $/boe over the long term and result in lowering incremental cost per incremental boe within a period of only 365 producing days in the current low oil price environment.