Petrophysical analysis of downhole logs requires accurate knowledge of matrix properties, commonly referred to as matrix adjustments. In organic-rich shale, the presence of abundant kerogen (solid and insoluble sedimentary organic matter) has a disproportionate impact on matrix properties because kerogen is compositionally distinct from all inorganic minerals that comprise the remainder of the solid matrix. As a consequence, matrix properties can be highly sensitive to kerogen properties. Moreover, the response of many downhole logs to kerogen is similar to their response to fluids. Relevant kerogen properties must be accurately known to separate tool responses to kerogen (in the matrix volume) and fluids (in the pore volume), to arrive at accurate volumetric interpretations. Unfortunately, relevant petrophysical properties of kerogen are poorly known in general and nearly always unknown in the formation of interest.
A robust method of “thermal maturity-adjusted log interpretation” replaces these unknown or assumed kerogen properties with a consistent set of relevant properties specifically optimized for the organic shale of interest, derived from only a single estimate of thermal maturity of the kerogen. The method is founded on the study of more than 50 kerogens spanning eight major oil- and gas-producing sedimentary basins, 300 Ma of depositional age, and thermal maturity from immature to dry gas (vitrinite reflectance, Ro, ranges from 0.5 to 4%). The determined kerogen properties include measured chemical (C, H, N, S, O) composition and skeletal (grain) density, as well as computed nuclear properties of apparent log density, hydrogen index, thermal- and epithermal-neutron porosities, macroscopic thermal-neutron capture cross section, macroscopic fast-neutron elastic scattering cross section, and photoelectric factor. For kerogens relevant to the petroleum industry (i.e., type II kerogen with thermal maturity ranging from early oil to dry gas), it is demonstrated that petrophysical properties are controlled mainly by thermal maturity, with no observable differences between sedimentary basins. As a result, universal curves are established relating kerogen properties to thermal maturity of the kerogen, and the curves apply equally well in all studied shale plays. Sensitivity calculations and field examples demonstrate the importance of using a consistent set of accurate kerogen properties in downhole log analysis. Thermal maturity-adjusted log interpretation provides a robust estimate of these properties, enabling more accurate and confident interpretation of porosity, saturation, and hydrocarbon in place in organic-rich shales.
Gong, Yiwen (The Ohio State University) | Mehana, Mohamed (Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, USA) | Xiong, Fengyang (The Ohio State University) | Xu, Feng (Research Institute of Petroleum Exploration and Development CO., LTD, CNPC) | El-Monier, Ilham (China National Oil and Gas Exploration and Development Corporation)
Rock elastic moduli are one of the major perspectives for the hydraulic fracturing design. Among all of them, Young's modulus and Poisson's ratio essentially control fracture aperture for the proppant placement. The objective of this work is to predict the elastic moduli by applying data mining techniques as a comparison to the experimental measurements. We have collected attributes representing the pore structure, mineralogy and geomechanical characteristics. We implemented classification techniques such as k-means, hierarchical and PAM (partition around medoids). PAM results in more evenly-distributed clusters compared to the rest. Artificial Neural Network (ANN) is used for regression. We formulated two scenarios; firstly, all the data is grouped into one group and the other involves performing the regression on the clustered data. Interestingly, both scenarios yield acceptable results. The classification results could guide the fracturing operations where clusters with high brittleness, low anisotropy and high microfracture intensity could be identified as fracture candidates. Still the main limitation to unleash the machine learning capabilities in this domain is the data scarcity
Unal, Ebru (University of Houston) | Rezaei, Ali (University of Houston) | Siddiqui, Fahd (University of Houston) | Likrama, Fatmir (Halliburton) | Soliman, M. (University of Houston) | Dindoruk, Birol (Shell International Exploration and Production, Inc.)
In the last decade, technical advancements have greatly improved the design and execution efficiency of well completions, leading to improved recovery from unconventional reservoirs. However, analyzing fracture diagnostic tests in unconventional plays are still challenging due to high uncertainty in predictive capabilities in the context of fracture dynamics during treatment. The main objective of this study is to identify fracture behavior during injection and pressure fall-off periods in hydraulic fracturing treatments and diagnostic fracture injection tests (DFIT), respectively.
In this study, discrete wavelet transformation (DWT) was used to analyze real field injection and fall-off data in the wavelet domain. The analyzed data are from multi-stage hydraulic fracturing operations and DFIT in unconventional horizontal wells. DWT coefficients reveal very crucial information related to the nature of the events within recorded signals; they also reveal various patterns that are hard to recognize otherwise. The high-frequency components of the pressure and rate signals (detail coefficients) that are calculated by the wavelet transformation determine localization and separation of various events. We compared the identified events for injection and fall-off periods with moving reference point (MRP) and G-function analysis, respectively.
The main advantage of our proposed approach is that it is based on real-time data and does not require any assumptions related to existing or created fractures. Also, it is very sensitive to physical changes in the system; thus, it reveals hidden information related to those changes. Consequently, the energy of detail coefficients represents several events at different frequencies. We used pseudo-frequency of wavelet coefficients as a diagnostic tool for an accurate comparison of fracture propagation and fracture closure events to determine similarities and differences between them. For example, the signal energy of detail coefficients from the wavelet transformation of hydraulic fracturing data demonstrates abrupt frequency changes during dilation or fracture height growth during fracture propagation. Therefore, we were able to identify those events by energy density analysis in both time and pseudo-frequency domains in an objective manner, which otherwise was not possible with conventional methodologies such as G- function derivative analysis.
This paper details the successful methodology for effective implementation of a new fracture diagnostic technique for fracturing operations or DFITs in unconventional horizontal wells. This new fracture diagnostic method does not require any reservoir or fracture pre-assumptions; it mainly relies on the pressure behavior, which is a result of various events at different frequencies. Pressure fall-off behavior of a DFIT gives essential information related to closure event of the created mini-fracture. Identification of these events at different pseudo-frequency ranges improves the understanding of the dynamic fracture behavior also the characteristics of the reservoir. Unlike many other diagnostic techniques, this data-driven approach requires minimum input/data for analysis. This approach also lends itself to real-time application quite easily.
Al-Alwani, Mustafa A. (Missouri University of Science and Technology) | Britt, Larry K. (NSI Fracturing) | Dunn-Norman, Shari (Missouri University of Science and Technology) | Alkinani, Husam H. (Missouri University of Science and Technology) | Al-Hameedi, Abo Taleb T. (Missouri University of Science and Technology) | Al-Attar, Atheer M. (Enterprise Products)
The goal of any hydraulic fracturing stimulation is to design and execute the appropriate treatment that is best suited for the stimulated reservoir. Selecting the best treatment must achieve the desired fracture geometry to maximize long-term well productivity and reserve recovery. The main objective of this study is to conduct detailed short and long-term production and well-to-well comparisons of the different types of fracture stimulation fluids in the Marcellus Shale play.
A database of more than 4,000 wells was integrated for this study. The wells were divided into four groups: water, gel, cross-linked, and hybrid fracs. Chemical data from FracFocus were gathered and processed then coupled with completion and production data to investigate the gas short and long-term production. Detailed monthly production data for the participating wells were captured from DrillingInfo database and utilized in this study.
This paper reports and compares the Marcellus gas initial production, the gas cumulative of the first month, first 6 months, first year, 2 years, and 5 years. The well productivity is tied to each hydraulic fracturing fluid type. The paper provides insights into the different completion trends in the Marcellus as well as the variations in stimulation parameters such as the volume of stimulation fluid and the amount of pumped proppants. The completion aspects of perforated lateral length are also taken into consideration and a comparison of the normalized production and stimulation parameters is also presented. The study shows that water fracturing fluids outperformed the other types of hydraulic fracturing fluids.
This paper introduces several data processing workflows that serve as a reference for individuals who are interested in mining and processing FracFocus database. It also documents the change in hydraulic fracturing fluid types and measures the effects of the fracturing fluid volume and total proppant pumped on the initial and cumulative production.
Hydrocarbon production from Shale formations has become an increasingly significant part of the global energy supply since 2010. With the advent of horizontal drilling and multiple-stage hydraulic fracturing, the Utica Shale, which underlies the Marcellus Shale as a natural source rock, is one of the most promising and productive shale plays in the US. However, very few academic papers discuss its geo-stress, pore pressure, permeability, and corresponding DFIT applications, which are essential for the development of the Utica Shale. The objective of this study is to use Diagnostic Fracture Injection Tests (DFITs) data from the field to analyze minimum in-situ stress, closure pressure, reservoir pore pressure, key reservoir properties and fracture geometry in the Utica Shale by different DFIT interpolation methods. The analysis results are compared and discussed in detail to investigate the features of each DFIT interpolation method. In addition, DFIT numerical simulation based on Variable Compliance Model is performed to predict induced fracture geometry and effective formation permeability in the Utica Shale.
DFIT is a commonly applied technique to analyze stress regimes and reservoir properties, while its interpolation can be challenging and difficult for different formations. DFIT interpretation for Shale formations is even more complex. In this study, first overviewing the geology of the Utica Shale and continuing to the summary of DFIT analysis and its governing equations, one can gain a better understanding of the methods and processes used to analyze our DFIT data targeting the Utica Shale. Tangent Line method, Compliance method, and Variable Compliance method are reviewed, and the corresponding assumptions for each method are examined, compared and discussed. Our DFIT data, which is acquired from a horizontal well targeting the Utica Shale, is interpreted by all methods to analyze minimum in-situ stress, closure pressure, initial reservoir pore pressure, key reservoir properties and fracture geometry. The DFIT results are then discussed and compared in detail to investigate the features of each method with its diagnostic signatures. Following that, the induced fracture geometry and the effective formation permeability are predicted by numerical simulation and sensitivity analysis, which also evaluate the impacts of wellbore storage, formation properties and fluid properties on simulated pressure and pressure derivative profiles.
The results from DFIT analysis are very encouraging. The Tangent Line method oversimplified leak off dependence and fracture stiffness, while the obtained minimum in-situ stress, closure pressure, pore pressure, fracture geometry and effective permeability are consistent with the diagnostic plots and our petrophysics studies. The Compliance method is able to identify mechanical closure, but it overestimates the minimum principal stress. The Variable Compliance method can capture the variance in fracture stiffness and pressure dependent leak off during progressive fracture closure, and its estimated closure pressure is an average of the results from the Tangent Line and the Compliance methods. The formation permeability of the Utica Shale is estimated by performing a history match of the pressure and pressure derivative profiles. The physics behind the DFIT simulation and sensitivity analysis is analyzed and discussed in detail. Our study can significantly improve the understanding of pressure/stress regimes, fracture geometry, and reservoir properties in the Utica Shale, as well as features of different DFIT interpolation methods. The knowledge and results demonstrated in this article will indefinitely assist operators in their optimization of multistage fracturing and horizontal drilling design in order to develop the Utica Shale more cost-effectively.
With the recent tremendous development in algorithms, computations power and availability of the enormous amount of data, the implementation of machine learning approach has spurred the interest in oil and gas industry and brings the data science and analytics into the forefront of our future energy. The idea of using automated algorithms to determine the rock facies is not new. However, the recent advancement in machine learning methods encourages to further research and revisit the supervised classification tasks, discuss the methodological limits and further improve machine learning approach and classification algorithms in rock facies classification from well-logging measurements. This paper demonstrates training different machine learning algorithms to classify and predict the geological facies using well logs data. Previous and recent research was done using supervised learning to predict the geological facies.
This paper compares the results from the supervised learning algorithms, unsupervised learning algorithms as well as a neural network machine learning algorithm. We further propose an integrated approach to dataset processing and feature selection. The well logs data used in this paper are for wells in the Anadarko Basin, Kansas. The dataset is divided into training, testing and evaluating wells used for testing the model. The objective is to evaluate the algorithms and limitations of each algorithm. We speculate that a simple supervised learning algorithm can yield score higher than neural network algorithm depending on the model parameter selected. Analysis for the parameter selection was done for all the models, and the optimum parameter was used for the corresponding classifier.
Our proposed neural network algorithm results score slightly higher than the supervised learning classifiers when evaluated with the cross-validation test data. It is concluded that it is important to calculate the accuracy within the adjacent layers as there are no definite boundaries between the layers. Our results indicate that calculating the accuracy of prediction with taking account the adjacent layers, yield higher accuracy than calculating accuracy within each point. The proposed feed-forward neural network classifier trains using backpropagation (gradient descent) provides accuracy within adjacent layers of 88%. Our integrated approach of data processing along with the neural network classifier provides more satisfactory results for the classification and prediction problem. Our finding indicates that utilizing simple supervised learning with an optimum model parameter yield comparable scores as a complex neural network classifier.
Fu, Xuebing (Goolsby Finley & Associates) | Bonifas, Paul (Goolsby Finley & Associates) | Finley, Andy (Goolsby Finley & Associates) | Lemaster, Julia (Goolsby Finley & Associates) | He, Zhiyong (ZetaWare, Inc) | Venepalli, Kiran (CMG Inc.)
Over the last decade, tight oil production has become significant with the success of horizontal drilling and hydraulic fracturing. However, the recovery factor of tight oil production remains very low and no standard secondary recovery method exists after primary depletion. We propose a new secondary recovery method: to use existing hydraulic fractures (every other fracture) in a horizontal well as gas injection and oil production sites to conduct
An ideal process of this method envisions a horizontal well centered in an enclosed reservoir, where the hydraulic fractures are evenly distributed along the well, parallel to each other. If the hydraulic fractures can be effectively isolated, and injection and production can be conducted through alternate fractures at the wellbore, then highly efficient flooding patterns can be created. Key questions include: Is there adequate injectivity and productivity in a sub-microdarcy reservoir? How far are the ideal reservoir conditions from reality? How difficult is it to isolate individual fractures within the wellbore?
Is there adequate injectivity and productivity in a sub-microdarcy reservoir?
How far are the ideal reservoir conditions from reality?
How difficult is it to isolate individual fractures within the wellbore?
In addressing these key questions, first, the success of a flooding process relies on reasonable injectivity and response time between the injector and producer – in this case the injector being one hydraulic fracture and the producer being an adjacent hydraulic fracture; both economical rates and reasonable communication time between adjacent fractures are demonstrated through analytical calculations and reservoir simulations in a typical well setting; nearly 100% recovery is achieved in the reservoir units between the fractures in a miscible flooding process. Second, actual reservoir conditions are incorporated in our study, focusing on direct fracture communications; the effects are demonstrated, and comparisons among different methods are made. Finally, potential challenges in operations are summarized and current technologies are reviewed; the gaps between the current settings and the required settings are demonstrated. Economic discussions are made, indicating positive scenarios with large tolerances.
With the rapid development of tight oil reservoirs, Enhanced Oil Recovery (EOR) technologies are urgently required to improve the recovery factor beyond primary depletion. An effective flooding process may be conducted if the hydraulic fractures can be used as injection and production ports. As a first attempt to envision an inter-fracture flooding process, key aspects are defined and examined, showing promising results. Inter-fracture gas flooding may become a standard secondary recovery technique for tight oil reservoirs and add significant reserves.
The US Department of Energy (DOE) has announced the selection of six projects to receive approximately $30 million in federal funding for cost-shared research and development in unconventional oil and natural gas recovery. The projects, selected under the Office of Fossil Energy's Advanced Technology Solutions for Unconventional Oil and Gas Development funding opportunity, will address critical gaps in the understanding of reservoir behavior and optimal well-completion strategies, next-generation subsurface diagnostic technologies, and advanced offshore technologies. As part of the funding opportunity announcement and at the direction of Congress, DOE solicited field projects in emerging unconventional plays with less than 50,000 B/D of current production, such as the Tuscaloosa Marine Shale and the Huron Shale. The newly selected projects will help master oil and gas development in these types of rising shales. This cement will prevent offshore spills and leakages at extreme high-temperature, high-pressure, and corrosive conditions.
Weijermans, Peter-Jan (Neptune Energy Netherlands B.V.) | Huibregtse, Paul (Tellures Consult) | Arts, Rob (Neptune Energy Netherlands B.V.) | Benedictus, Tjirk (Neptune Energy Netherlands B.V.) | De Jong, Mat (Neptune Energy Netherlands B.V.) | Hazebelt, Wouter (Neptune Energy Netherlands B.V.) | Vernain-Perriot, Veronique (Neptune Energy Netherlands B.V.) | Van der Most, Michiel (Neptune Energy Netherlands B.V.)
The E17a-A gas field, located offshore The Netherlands in the Southern North Sea, started production in 2009 from Upper Carboniferous sandstones, initially from three wells. Since early production history of the field, the p/z plot extrapolation has consistently shown an apparent Gas Initially In Place (GIIP) which was more than 50% higher than the volumetric GIIP mapped. The origin of the pressure support (e.g. aquifer support, much higher GIIP than mapped) and overall behavior of the field were poorly understood.
An integrated modeling study was carried out to better understand the dynamics of this complex field, evaluate infill potential and optimize recovery. An initial history matching attempt with a simulation model based on a legacy static model highlighted the limitations of existing interpretations in terms of in-place volumes and connectivity. The structural interpretation of the field was revisited and a novel facies modeling methodology was developed. 3D training images, constructed from reservoir analogue and outcrop data integrated with deterministic reservoir body mapping, allowed successful application of Multi Point Statistics techniques to generate plausible reservoir body geometry, dimensions and connectivity.
Following a series of static-dynamic iterations, a satisfying history match was achieved which matches observed reservoir pressure data, flowing wellhead pressure data, water influx trends in the wells and RFT pressure profiles of two more recent production wells. The new facies modeling methodology, using outcrop analogue data as deterministic input, and a revised seismic interpretation were key improvements to the static model. Apart from resolving the magnitude of GIIP and aquifer pressure support, the reservoir characterization and simulation study provided valuable insights into the overall dynamics of the field – e.g. crossflows between compartments, water encroachment patterns and vertical communication. Based on the model a promising infill target was identified at an up-dip location in the west of the field which looked favorable in terms of increasing production and optimizing recovery. At the time of writing, the new well has just been drilled. Preliminary logging results of the well will be briefly discussed and compared to pre-drill predictions based on the results of the integrated reservoir characterization and simulation study.
The new facies modeling methodology presented is in principle applicable to a number of Carboniferous gas fields in the Southern North Sea. Application of this method can lead to improved understanding and optimized recovery. In addition, this case study demonstrates how truly integrated reservoir characterization and simulation can lead to a revision of an existing view of a field, improve understanding and unlock hidden potential.
This paper describes challenges faced in a company’s first deepwater asset in Malaysia and the methods used to overcome these issues in the planning stage. This paper discusses the successful application of managed-pressure drilling (MPD) in the basin with reduction in risks and well costs. This paper discusses how managed-pressure-drilling (MPD) technology led to cost savings in two wells drilled in the Hai Thach gas field offshore southern Vietnam. This paper describes how a technique known as applied-surface-backpressure managed-pressure drilling (ASBP-MPD) can alleviate the limitations of conventional deepwater well control. The complete paper describes a recent directional coiled-tubing drilling (DCTD) job completed for an independent operator in the Appalachian Basin.