bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting Long-term Production Behavior of the Marcellus Shale
Authors: Nelson B. , Belyadi F. , Mashayekhi A. , Aminian K. , Ameri S. 
Published: June 2014

Abstract
The production behavior of horizontal wells producing from Marcellus shale has not been well established due to limited production history. As a result a simple method for predicting the long-term production would be of interest to the industry. Several DCA models have been proposed specifically for unconventional gas reservoirs. However, their reliability to predict the long-term production for Marcellus shale horizontal wells has not been established. In this study, production and completion data from a number of horizontal wells completed in Marcellus shale were collected. In addition, the properties of Marcellus shale were measured in laboratory with precision equipment designed for unconventional formations. The field data as well the laboratory measured properties were utilized in conjunction with a commercial numerical simulator to predict the long-term production behavior of horizontal wells producing from Marcellus shale. The numerical model allowed for inclusion of adsorbed gas, multiple hydraulic fracture stages, as well as dual porosity behavior. The predicted production profiles were then utilized to evaluate the applicability of the various DCA models. Subsequently, a technique was developed to estimate the parameters of the DCA model to predict the long-term production based on the early production history as well as the key reservoir parameters. Finally, the results of the DCA model predictions were compared to the history-matched simulation model predictions for confirmation.
Identifying Hydrocarbon Zones in Unconventional Formations by Discerning Simpson's Paradox
Authors: Ma Y. Zee , Moore William , Kaufman Peter , Wang Yating , Gurpinar Omer , Luneau Barbara , Gomez Ernest 
Published: June 2014

Abstract
This article is concerned with the identification of hydrocarbon zones in unconventional plays through lithofacies classification using wireline logs. Identification of hydrocarbon zones in unconventional formations is often different than in conventional formations. For example, in conventional reservoirs, the fractional volume of clay (Vclay) is generally correlated strongly and positively with gamma ray (GR); high GR values commonly indicate low hydrocarbon potential. In unconventional reservoirs, however, Vclay is often correlated weakly with GR, and sometime even negatively. There are several possible explanations for this change of correlation. In source-rock reservoirs, high GR often indicates high total organic carbon (TOC), kerogen, and gas or oil concentrations. In some tight-gas sandstone reservoirs, hydrocarbon-bearing sandstones have abnormally high GR.
The change of the correlation between commonly used wireline logs in conventional formations to a different correlation between those same logs in unconventional formations is, in fact, a manifestation of the Simpson's paradox—a counterintuitive phenomenon in probability and statistical data analysis. Here we show how to classify lithofacies and identify high-potential hydrocarbon zones in unconventional resources by discerning the Simpson's paradox when analyzing relationships between wireline logs. We present examples from a tight-gas sandstone reservoir and a shale-gas reservoir.
Analyzing Variable-Rate Flow in Volumetric Oil Reservoirs
Authors: Elgmati A. R. , Flori R. E. , Kabir C. S. 
Published: June 2014

Abstract
Estimating average-reservoir pressure (pav) and its evolution with time is critical to analyzing and optimizing reservoir performance. Conventionally, selected wells are shut in periodically for buildup tests to determine pav over time. Unfortunately, shutting-in wells leads to loss of production. Today, however, real-time surveillance—the continuous measurement of flowing pressures and rate data from the oil and gas wells—offers an attractive alternative technique to obtain average-reservoir pressure while avoiding revenue loss.
A direct method for estimating pav from flowing pressures and rate data is available. However, the method is for an idealized case that assumes constant production rate during pseudosteady-state (PSS) flow, which is not generally true for real wells. This paper extends that approach so that it can be used to analyze field data with variable rates/variable pressure during PSS flow. This approach is based on a combination of rate-normalized pressure and superposition-time function. The mathematical basis is presented in support of this approach, and the method is validated with synthetic examples and verified with field data.
This modified approach is used to make direct estimation of average-reservoir pressure that uses flowing pressures and production rates during PSS flow, allowing the classical material balance calculations to be performed. These calculations, in turn help determine the reserves, recovery factor, and reservoir drive mechanisms, allowing the reservoir performance and management to be properly evaluated. Furthermore, this method can be used to calculate both connected oil volume and reservoir drainage area as a function of time. Finally, this approach provides a reasonable estimation of the reservoir's shape factor.
Artificial Intelligence (AI) Assisted History Matching
Authors: Shahkarami Alireza , Mohaghegh Shahab D. , Gholami Vida , Haghighat Sayed Alireza 
Published: June 2014

Abstract
History matching is the process of adjusting uncertain reservoir parameters until an acceptable match with the measured production data is obtained. Complexity and insufficient knowledge of reservoir characteristics makes this process time-consuming with high computational cost. In the recent years, many efforts mainly referred as assisted history matching have attempted to make this process faster; nevertheless, the degree of success of these techniques continues to be a subject for debate.
This study aims to examine the application of a unique pattern recognition technology to improve the time and efforts required for completing a successful history matching project. The pattern recognition capabilities of Artificial Intelligence and Data Mining (AI&DM) are used to develop Surrogate Reservoir Model (SRM) for utilization as the engine to drive the history matching process. SRM is an intelligent prototype of the full-field reservoir simulation model that runs in fractions of a second. SRM is built using a handful of geological realizations.
In this study, a synthetic reservoir model of a heterogeneous oilfield with 24 production wells and 30 years of production history was used as the ground truth (the subject and the goal of the history match). An SRM was created to accurately represent this reservoir model. The history matching process for this field was performed using the SRM and by tuning static data (Permeability). The result of this study demonstrates the capabilities of SRM for fast track and accurate reproduction of the numerical model results. Speed and accuracy make SRM a fast and effective tool for assisted history matching.
Proper Inclusion of Hydraulic Fracture and Unpropped Zone Conductivity and Fracturing Fluid Flowback in Single Shale Oil Well Simulation
Authors: Zanganeh Behnam , Ahmadi Mohabbat , Hanks Catherine , Awoleke Obadare 
Published: June 2014

Abstract
Horizontal drilling and multi-stage hydraulic fracturing have made the commercial development of nano-darcy shale resources a success. Although current production volumes are promising but currently published simulation results show single-digit recovery factors under depletion. Such low recovery factors highlight the importance of accurate modeling of fluid flow and well performance for wells draining such resources. In this study, explicit hydraulic fracture modeling based on data from Eagle Ford shale oil window was performed by linking hydraulic fracture design and reservoir simulation softwares. Simulated production profiles of a homogenous simplified profile and an actual conductivity profile generated by hydraulic fracture design software were compared. The amount of fracturing fluid flowback during the first year of production was examined. The effect of unpropped zone conductivity on production performance was also investigated.
The modeling results highlight the pitfalls of misrepresenting the hydraulic fracture with a constant conductivity profile. It also emphasizes the importance of appropriately modeling the amount of recovered fracturing fluid by correctly incorporating fracture propagation predicted by hydraulic fracture design software into the flow simulation model.
Oil and water production trends for a single hydraulic fracture stage were observed with and without considering fracturing fluid flowback. Results of a single stage hydraulic fracture simulation showed that ignoring flowback overestimated oil recovery by about 17%. Assuming a constant permeability in the hydraulic fracture plane resulted in overestimation of oil recovery by almost 25%. The conductivity of the unpropped zone affects the recovery factor predictions by as high as 10%. For the case investigated, about 25% of the fracturing fluid was recovered during the first 2 months of production. Increasing the shut in time after fracture stimulation from 6 hours to 24 hours resulted in an increase of flowback volume by 5%.
This paper addresses the importance of proper modeling hydraulic fracture conductivity in simulation of hydraulically fractured shale wells. It also highlights the importance of the amount and duration of fracturing fluid flowback, and explains how fracturing and fluid flow modeling softwares can be linked for better well performance predictions.
Surfactant Preflood to Improve Waterflooding Performance in Shale Formations
Authors: Morsy Samiha , Sheng J. J. 
Published: June 2014

Abstract
The lower primary oil recovery from shale formations accelerated the application of waterflooding technology as a secondary recovery mechanism at earlier time compared with conventional reservoirs. This situation suggests the EOR surfactant technology designed to promote additional oil recovery from fractured carbonate formations is a fit for these shale formations that are characterized by complex fractured lithology. The presented study investigates the idea to incorporate appropriate surfactant formulations at a low dosage as a preflood to waterflooding in shale formations. If properly designed, such surfactant in the preflood fluid will penetrate into the high oil saturation matrix or natural fracture region and accelerate the extraction of the oil in place by rapid imbibition. This extracted oil can readily move from the matrix, into the propped fracture system, and then is produced. Another benefit of the preflood surfactant is engineered in such that it leaves the matrix or natural fracture face water wet to facilitate oil movement during production.
This paper presents a study of a series of surfactant additives developed for extracting additional oil. Over 10 of special customized product blends were evaluated in laboratory for their effectiveness in increasing oil recovery of Bakken formation. Only one surfactant (Stim aid A) that was compatible with Bakken formation brine and crude oil, so it was the only one used for spontaneous imbibition experiments. The average porosity of the used Bakken reservoir samples was 5.8% with an average bulk density of 2.75 g/cc. The Bakken rock samples were pre-treated with different surfactant solutions and then used for water spontaneous imbibition experiments. During the spontaneous imbibition, the maximum oil recovery was from the samples that were pre-treated with 2 wt% of (Stim aid A) surfactant. The measured contact angles on Bakken samples showed an alteration in rock wettability that interpreted the improvement of Bakken higher recovery factors from spontaneous imbibition in surfactant solutions compared with brine solutions only.
Potential Pitfalls in Exploration and Production Applications of Machine Learning
Authors: Harris Cole 
Published: June 2014

Abstract
Exploration and production applications of machine learning algorithms are varied and numerous. However less attention has been given to the underlying assumptions critical to the application of such techniques. As the breadth of applications increases, it is critical to understand those characteristics of the problem and data that may impact the results.
Independent of the particulars of any specific algorithm, the standard model development and evaluation process may be described as follows. From a set of data points consisting of features and a response, a machine learning algorithm produces a model that may then be used to compute predicted responses on new data. This model can be applied to additional data for which the response is available, and performance estimated by comparing the predicted and actual response. However the reliability of this estimate may be strongly dependent on the statistical characteristics of the data. If the observations are not independent, then the results may not reflect performance in application.
To explore the impact of the violation of the assumption of independence on predictive model development and evaluation, standard machine learning algorithms were used to develop models from synthetic time series data and real monthly oil production data from the Wolfcamp play in the Midland basin. For both, standard approaches to model evaluation fail. For the oil production data, an alternative approach to model development and evaluation is shown to produce both more reliable estimates of model performance and improved model performance.
A Step Ahead Of 4D: The Integration Of Basic And Analytical Techniques To Drive The Changes In The Reservoir Simulation Model History Match: Case Study
Authors: Gomez Alonso Hector , Avila Runer , Peppard Bret , Gee Vicki , Baima Joao , Simone Alessandra , Tejera-Cuesta Pablo 
Published: June 2014

Abstract
This paper describes how the integration of basic, analytical and numerical reservoir engineering techniques in the Salema Field, and to really "understand reservoir dynamics". Because Salema is a mature field with a water injector, it was even more important to determine the presence of additional opportunities.
The application of traditional techniques such as conventional production analysis, diagnostic and Chan plots, material balance, Build-ups/Fall-off tests allowed the confirmation of different key events and water production mechanisms, to characterize the basic description of reservoir dynamics. The understanding gained was incorporated into the simulation model.
The methodology helped to minimize the number of uncertainties, narrowing down the variables that had a considerable impact on the history match, diminishing the number of potential realizations, and more importantly ended up in a more consistent and better understanding of the production behavior of the field.
Previous models were not able to reproduce the water production/reservoir pressure observed in some of the existing wells, clearly suggesting that the injected water volumes were not represented correctly in the reservoir.
The methodology can be considered as an integrated workflow towards the construction of any 3D reservoir simulation model, as it is simple and easy to follow. The 4D results obtained six months later, confirmed the results obtained from what the model predicted.
This case study is another example that success in subsurface disciplines depends more on the correct understanding of the reservoir than the degree of complexity with which it is modeled, using reservoir simulation to confirm the achieved understanding rather than just getting a history match.
All the integrated work resulted in the identification of two additional opportunities that will increase the final RF to close to 40% and contributed to extend the field life until 2022. Such opportunities have later been confirmed by the 4D seismic interpretation.
Due to Brazilian regulations the original well names are omitted in this document, instead generic well names have been used.
The Role of Boundary Conditions And Characteristic Length On Imbibition in Both Sandstones And Limestones
Authors: Kim Tae Wook , Kovscek A. R. 
Published: June 2014

Abstract
Spontaneous imbibition is generally defined as the displacement of a non-wetting phase by a wetting phase through dominant capillary suction forces in porous media. Spontaneous imbibition is a critical phenomenon in oil recovery from strongly water-wet and highly fractured reservoirs. The rate of spontaneous imbibition depends on permeabilities, relative permeability curves, block shapes, boundary conditions, fluid viscosity, interfacial tension, and wettability. The purpose of this study was to investigate the effect of characteristic length, boundary conditions, mobility ratios, and initial water saturation (Swi) conditions on the rate of spontaneous imbibition. We conducted spontaneous countercurrent and co-current water imbibition into cylindrical Berea sandstone (water-wet), Indiana limestone (mixed-wet), Texas Creme limestone (mixed-wet) with different boundary conditions, characteristic length, and Swi from 0 to 0.5. Brine (1 wt% NaCl) is used as the wetting fluid. Also, air, n-decane (n-C10), and Blandol are used as non-wetting fluids, respectively, to compare the effect of mobility ratio. To measure the rate of imbibition and oil production from each core, we used an X-ray computerized tomography (CT) scanner and an imbibition cell. The different mobility ratios show different oil production histories within the same time-scale. The observed 2-D and 3-D in-situ saturation profile histories of each rock clearly show different imbibition patterns for each boundary condition. Generally, low permeability limestones have more heterogeneous features than sandstones. We used three different boundary conditions including (1) all faces open (AFO), (2) two ends open (TEO, i.e., inlet and outlet face), and (3) one end open (OEO, i.e., one face of the core) systems. Characteristic length had an inverse effect on the rate of spontaneous imbibition. Experiments exploring the role of boundary conditions showed the effect of total open surface area for the oil production with different Swi. Finally, a scaling exercise to reduce data using various definitions of dimensionless time was introduced to reduce all experimental data. Further study is needed, however, to suggest an improved scaling group.
Correlation of Stimulated Rock Volume from Microseismic Pointsets to Production Data - A Horn River Case Study
Authors: Rahimi Zeynal A. , Snelling P. , Neuhaus C. W. , Mueller M. 
Published: June 2014

Abstract
Hydraulic fracture monitoring from microseismic allows operators to optimize completions through a clear understanding and correlation of the reservoir response to stimulation. Furthermore it helps operators to improve production and avoid out of zone growth by identifying patterns of fluid movement, fracture growth and connectivity. These critical insights allow refinements to the treatment plan, and provide useful insights for long-term improvements regarding well spacing, well design, and completion design.
Shale reservoirs with very low permeability in the nano darcy range require a large fracture network to increase well performance. In these reservoirs, unless natural pre-existing fractures and faults have been reactivated and hydraulically opened to create a complex and well-connected network, pore pressure changes do not permeate far from the fractures. As a result, the microseismic pointset roughly corresponds to the size of the real fracture network which offers a means to estimate the stimulated rock volume (SRV). Although the producing fracture network could be smaller than the total SRV by a substantial percentage, it is expected that the effective network and the total SRV show a positive correlation. However, SRV is not the only indicator of well productivity. In a given SRV, the quality of the reservoir and parameters such as fracture conductivity and fracture spacing will affect production and can have a major impact on recovery calculations.
In this study, stimulated rock volumes obtained from microseismic pointsets are correlated with actual field production. The correlations are used to illustrate how this concept can optimize treatment design, well spacing, and stage spacing through correlation of the reservoir response to hydraulic fracturing and production data. The correlation between production and SRV for each well shows that larger SRVs result in higher well production regardless of the percentage of the SRV that contains proppant filled fractures. The direct relationship of the microseismic pointsets and production can be used to predict a new well's potential productivity immediately upon completion of the stimulation job. This suggests that a key completion strategy is to create a large and effective SRV to provide maximum recovery and well performance monitored microseismically to provide production prediction.
Towards Dynamic Control of In-situ Combustion: Effect of Initial Oil and Water Saturations
Authors: Kudryavtsev Philipp , Hascakir Berna 
Published: June 2014

Abstract
The performance and probability of success of in-situ combustion (ISC) is highly dependent on a multitude of parameters that define the reservoir conditions. The presence of reservoir heterogeneities makes it, therefore, practically impossible to predict a set of operational parameters that would provide efficient ISC performance. Dynamic control, whereby operational parameters are adjusted based on the reservoir conditions at the predicted combustion-front position, has the potential to significantly improve ISC performance. One-dimensional combustion tube experiments are the quickest method to determine the potential candidates for ISC. However, experiments conducted at average reservoir initial conditions do not provide data on the range of required operational parameters in a heterogeneous setting. Therefore, lab-scale experiments should be extended to screening tests by evaluating the ISC performance at the upper and lower limits of the reservoir initial conditions. To pave the way for dynamic control of ISC, this study investigates the effect of varying initial water saturation and initial oil saturation with one-dimensional combustion tube experiments on a bitumen sample (1.018 g/cm3) with high asphaltene content (∼35 wt%).
ISC performance change with varying initial oil and water saturations are discussed in terms of effluent gas composition, temperature profiles, total experiment time, oil recovery, behavior of fluid (oil, water, steam, and gas) front movements, X-Ray cross sectional images on postmortem samples, and the level of oil upgrading at different initial fluid saturations.
Our results indicate that the presence of initial water assists the combustion process and around 30% initial water enhances the ISC performance which can be maintained by steam injection prior to ISC if reservoir initial saturation is less than 30%. Moreover, initial oil saturation has been found to be the most critical parameter which defines the amount of fuel formed, and as a consequence, the heat generated.
The findings provide a significant improvement in our understanding of optimum operational conditions for a range of initial fluid saturations, bringing us one step closer to dynamic control of field-scale ISC.