Shale Assets Research Articles

A flexible and scalable model to improve decision quality in shale plays

Shale asset investment decisions are difficult to model because they are portfolios of options under complex and time-evolving uncertainties. Sequential development decisions must balance short-term cash flow with long-term value creation. Typical assets consist of thousands of locations, making exact analysis impossible. Consequently, decision support tools are very simple, usually consisting of decision trees, fixed price decks, and no midstream constraints.We develop a heuristic that maps the current information of an asset, such as inventory, prices, and estimated production, to a well schedule. We combine the heuristic with Monte Carlo simulation and decision trees to form a shale field development model that includes all relevant features while being scalable to realistic problem sizes. To demonstrate our method's performance, we create a sample shale asset with 370 wells, price and production uncertainty, midstream constraints, and 30 decision periods. On the sample problem, our method exhibits first-order stochastic dominance over the decision tree approach and improves expected value by 407%. Using our modeling approach will improve overall decision quality in shale asset management.

Well engineering for tight oil development in shale asset in North America

Well engineering for tight oil development in shale asset in North America

Generating pseudo well logs for a part of the upper Bakken using recurrent neural networks

To develop a reservoir, we need to understand the distribution of key reservoir properties. Those formation properties are mostly derived from well log data. However, obtaining well log data is so expensive that the data we acquire is never sufficient. Therefore, we need to create pseudo data from real measured data. One approach is to generate synthetic well logs using recurrent neural networks (RNNs). Though relatively nascent, it is proven that RNNs allow for the generation of well logs with reasonable accuracy at a fraction of the price. Currently, gamma ray is the most cultivated well log in the E&P process today (owing to its decisive role during drilling operations and affordable price). All the same, a more extensive log suite is required to comprehend the producibility and frackability of hydrocarbon reservoirs like shale reservoirs.In this study, an upper Bakken database of 97 wells which contain gamma ray, bulk density, photoelectric index, density porosity, neutron porosity, true resistivity, compressional and shear slowness was used to develop deep learning models apt to creating synthetic logs for another set of 40 wells in the upper Bakken. The latter wells only had measured gamma ray. Using petroleum engineering concepts and RNNs, data-driven models capable of generating the remaining logs were created. The data-driven models were validated using wells with logs that have been removed from the database to serve as blind validation wells. The performance of the models (5–10 Mean Average Percentage Error and R-squared of 0.6–0.8) suggests the workflow described in this study is a viable way to maximize the value of a gamma ray log. With the ability to generate logs of similar quality for all wells in a shale asset (starting only with gamma ray), superior reservoir evaluation and thus improved reservoir management can be achieved.

SPE Strong: Strengthening Our Core: Strengthening Knowledge Transfer in Unconventional Resources

President's column To collect, disseminate, and exchange technical knowledge—are the first seven words of SPE’s mission statement. These are the three fundamental objectives of our Society. We proactively seek out the technical knowledge that not only our members need, but that the industry requires to meet the world’s demand for oil and gas. Collecting this information can be challenging, especially if those who have it are not willing or able to share it. This proved especially challenging in the area of unconventional shales when SPE began to hold conferences on the subject nearly a decade ago. At that time, papers authored by knowledgeable operators were few and far between. The rationale adopted by many operators was that any technical information about their company’s shale assets was confidential, and by withholding it, they could gain a small competitive advantage. This was also problematic for academia trying to solve the complex problems of unconventional reservoirs. Access to data was minimal unless university projects were directly funded by an operator to conduct research. Even if academics received relevant data and funding, most were then restricted from publishing their research by the operator. Today the collection of available data and findings from oil and gas shales is much improved. Recently, ConocoPhillips donated a cleaned and anonymized data set that comprises internal drilling, completions, and other well data from US unconventional reservoir plays to The University of Texas at Austin. This data donation allows UT professors to formulate real-world data analytics problems for their student teams to solve. Greg Leveille, chief technology officer at ConocoPhillips, explains, “The oil and natural gas industry is increasingly utilizing leading-edge data science techniques to find, develop, and produce hydrocarbon resources more effectively. ConocoPhillips believes it is important to support universities’ efforts to build programs to teach petroleum engineering students the foundational data science skills they’ll need to maximize their contributions to the E&P industry after they graduate.” Greg’s attitude toward sharing our unconventional data sets with universities is another excellent step in the right direction and one that I hope will expand to additional operators and academic institutions. Within SPE, the ways that we disseminate and exchange information are also much better than they were 10 years ago, but are they good enough? Should we, as a society, be doing more with the knowledge transfer of the unconventional shales? The map shows the discovered unconventional oil and gas basins around the world. Each one has its own unique geological, technical, and economic challenges to future exploitation. While JPT provides informative articles on numerous basins, there are a few regions where SPE is planning events focused on additional knowledge transfer in unconventionals.

An Integrated Machine-Learning Approach to Shale-Gas Supply-Chain Optimization and Refrac Candidate Identification

Summary The unsteady recovery of oil and gas prices in early 2017 led to an increase in drilling and hydraulic–fracturing operations in liquid–rich shale plays in North America. As field–development strategies continue to evolve, refracturing and infill–well drilling must be carefully combined to optimize shale–project profitability. Moreover, operators must bear in mind the undulating natural–gas demands persisting in an oversupplied shale–gas environment. In this paper, we use data–driven approaches to predict successful refracturing candidates and local gas demand for the second–tier optimization of a shale–gas supply–chain network. A strategic–planning (SP) model is developed for optimizing the net present value (NPV) of a case–study shale–gas network in the Marcellus Play. This SP model uses a mixed–integer–nonlinear–programming (MINLP) formulation developed in the General Algebraic Modeling System (GAMS, Release 27.1.0.2019). This model relies directly on input from reservoir simulation, local–gas–demand forecast, water–availability forecast, and natural–gas and West Texas Intermediate (WTI) crude–oil price forecasts. Before reservoir simulation, machine learning (ML) is used to predict successful refracturing candidates, using a feed–forward neural network (NN), random–forest (RF) classifier, and a t–distributed stochastic–neighbor–embedding (t–SNE) visualization technique. Using the obtained results, best–practice field–development strategies are implemented in the area of interest (AOI) using reservoir simulation. Local gas demand is forecasted using a long–short–term–memory (LSTM) recurrent NN (RNN) that uses a multivariate data set created from local and global variables affecting shale–gas demand. A water–management structure is also developed for the optimization framework. Using a 300–well data set (with 17 input features), successful refracturing candidates were proposed according to the joint outcome of an optimal 17/23/128/2 feed–forward NN, a t–SNE plot, and a techno–economic review. After ranking F1 scores, the developed NN outperforms the RF and support–vector–machine (SVM) algorithms for frac/refrac–well classification. The developed 32/256/128/120 LSTM model showed at least a 93% (±1%) prediction performance using three or five input features. The results illustrate the ability of the developed LSTM model to accurately predict local gas demands during periods of high or low gas demand. After SP optimization over a 10–year planning horizon, the economic results indicate an NPV of USD 481.945 million, using the proposed physics–data–driven–based approach. An NPV of USD 611.22 million is obtained when no ML was used. The results reveal that the application of ML to strategic planning can prevent erroneous feedback of project profitability while allowing early–time decision making that maximizes shale–asset NPV.

Anadarko’s Digital Legacy Offers a Template for Others

Up to the time of its takeover by Occidental Petroleum, Anadarko was known as one of the biggest cheerleaders of the industry’s so-called digital transformation that is banking on new software technologies to expand tight margins. Last year, Anadarko opened its doors to share the story of how it put together a new 50-person team of data scientists, software developers, and petrotechnical staff to realize this vision—known as the Advanced Analytics and Emerging Technologies (AAET) group. The group’s man-date boils down to looking for new ways to solve old problems. Oxy has not shared many details on how it will combine the two companies based in the Houston area, which means it is unclear whether its digital priorities will marry up with the work of its newest addition. And with the integration process now officially under way, Anadarko has offered this final look at what the AAET group has been able to•accomplish.• “We think 80% of Anadarko’s capital program is affected in some way, shape, or form by using AAET,” said Sanjay Paranji, the company’s chief technology officer and leader of the digital group. As evidence of the scale of internal adoption, he added that 70% of the company’s petrotechnical staff use at least one of the new data analytics and aggregation platforms that the group built since it was formed in 2016. Behind these high-level numbers is a layered strategy that Anadarko has used to solve the great “buy vs. build” challenge that all companies diving into the ever-expanding digital landscape have had to struggle with. The overriding philosophy is broken into three parts: Initiatives with the highest potential impact on the bottom line are developed largely internally. This has led the company to install its own electronic drilling recorders on the rigs it operates and design software that can classify rig states to benchmark rig equipment (e.g., drill bits, rotary steerables) and crew performance. Anadarko has also internally led the development of large analytics and field surveillance platforms that have been rolled out to its biggest deepwater and onshore shale assets. And in this same bucket is a major project to increase the resolution and quality of geologic and geophysical data sets. To avoid reinventing the wheel, the company evaluated around 250 technology startups each year to see if their wares matched the company’s priorities. More than 500 of Anadarko’s plunger lift gas wells in the DJ Basin are now controlled through edge devices made by a Silicon Valley startup called Kelvin Inc. The operator launched projects with an Austin-based startup called Seismos, which uses acoustic signals to evaluate hydraulic fracture performance in horizontal wells. For digital projects, or pieces of projects, that represent less potential value, the company has turned to crowdsourcing, also known as open innovation. More than 2,000 people from around the world have helped Anadarko create phone apps, an internal Wikipedia-like portal to educate employees and other components of larger analytics projects. The company’s new mud log analysis program was designed by a Brazilian police detective who specializes in bank fraud.

Data driven techno-economic framework for the development of shale gas resources

This work presents a data driven techno-economic framework that combines upstream, midstream and downstream operations to optimize profitability of shale projects. This framework is illustrated using a shale gas supply chain structure planned for development and integration in an over-supplied gas market. Field development strategies are developed based on applying machine learning techniques to an existing field. Alternative development strategies are implemented on the integrated production-modeling platform RESOLVE–REVEAL to simulate hydrocarbon/water production. Long-short term memory (LSTM) neural networks are developed to predict gas demand and freshwater availability. Long-term strategic planning is achieved using a mixed-integer non-linear programming (MINLP) formulation. Results indicate a net present value (NPV) of 205.56 MMUS$ for optimal infrastructure design, gas and liquids transportation and distribution, and water management after 54-well integration. Additionlly, results provide an optimal gas storage schedule that supports shale asset profitability. Application of this techno-economic approach improves profitability projections for shale enterprises.

Operator Executives Share Haynesville Development Strategies

Indigo Natural Resources, Aethon Energy Management, and Rockcliff Energy II are among the most active operators in the revived Haynesville Shale of North Louisiana and East Texas. And most people outside of the region have likely never heard of them. Such is the story of the bulk of the top producers in the dry-gas-rich Haynesville, which differs from its peer group of big shale plays that are led by majors and big independents. While Chesapeake Energy, XTO Energy, and BP each has a presence there, it is the lesser-known privately-owned, largely pure-play operators—with their new completions and midstream partnerships—that have accounted for much of the play’s output increases. Those companies began entering the play a few years ago when many of the bigger operators were turning away from dry gas. The mergers and acquisitions continued through 2018, when, notably, Aethon struck a deal to buy QEP Resources’ Haynesville-Cotton Valley acreage for $735 million and BP more than tripled its Haynesville position through its purchase of BHP Billiton’s US shale assets. Indigo, in particular, over the past 4 years has aggressively leased and acquired acreage in North Louisiana. The company targeted the Louisiana portion of the play because it benefits from the ability to drill north-south with cross-unit laterals and forced pooling, said Frank D. Tsuru, president and chief executive officer of Indigo, during a recent SPE Gulf Coast Section panel discussion of Haynesville executives in Houston. The Houston-based company has amassed 435,000 net acres and, through Momentum Midstream, has “built a fully integrated, very large-scale mid-stream business to complement it,” said Tsuru, who also serves as Momentum’s CEO. Indigo currently produces around 1.3 Bcf/D, up from 200 MMcf/D in 2016, with proved reserves of 4.7 Tcf. Preston Phillips, vice president of acquisitions and divestitures of Dallas-based Aethon, said his company took “a contrarian investment strategy” that involved building a position in Haynesville when it could basically “buy upside for nothing” as others had given up on the play. “Now that we’re a significant player, we will continue to make acquisitions and bolt on to our position,” he said. Aethon currently has 186,000 net acres in the Haynesville consisting of a large position in North Louisiana as well as a less mature position in the Shelby Trough play of East Texas, a portion of which it bought from Rockcliff Energy. Most of its position either has the Bossier formation underneath it or Cotton Valley sands above it, he said. “We’re already doing Bossier development and seeing great success. The Cotton Valley’s got a lot more water, so there’s some cost issues to unlock there, but we’re working hard on it.” Around 70% of the operator’s inventory has been drilled using 7,500-ft and 10,000-ft laterals. “Fortunately, there’s a large number of LNG plants and petrochemical projects that are being developed in the Gulf Coast,” Phillips said. “And that demand is why we think we’re very well positioned being in the Haynesville play relative to the other basins.”

Shale EOR Delivers, So Why Won’t the Sector Go Big?

The oil is there. The gas is nearby. The process is proven. But is there an appetite to put it all together and redefine what it means to be a shale producer? This is the key question looming over the future of enhanced oil recovery for tight shale reservoirs, or simply shale EOR. To answer it, unconventional oil producers are trying to weigh the options from what amounts to a complicated pros-and-cons list. Developing a shale EOR program may mean drawing resources away from new exploration projects that have quicker returns, the same conundrum that has stymied the US refracturing market. On the other hand, shale EOR boasts impressive economics for companies willing to reinvest in land and wells already paid for. This financial tug-of-war has been playing out in the shale sector since the spring of 2016. That was when Houston-based EOG Resources let it be known that its shale EOR program was boosting production from vintage horizontal wells in its Eagle Ford Shale asset in south Texas. News of the development quickly made the operator synonymous with shale EOR. It is now widely understood that all of these projects rely on the huff-and-puff injection process using natural gas as the special agent that can unlock those additional barrels. Other key details are coming to light as well—such as the expanding scope of success. In a recent quarterly earnings statement, EOG said it continues to see “strong results” from around 150 EOR wells, more than a third of which were converted in 2018. Analysts and engineering consultants have found about 100 other wells in the Eagle Ford that several other operators have converted into huff-and-puff injectors. “It’s kind of incredible to see the data,” said John Watson, the senior research analyst who put together a report late last year that highlighted production details of shale EOR projects. After physically combing through filings at the Texas Railroad Commission (since they are not available to download), he found dozens of pad wells that saw a combined 10-fold rise in production above their trough. Among the standouts, a group of 11 wells that reached a combined peak production rate in December 2011 of about 90,000 bbl a month. By August 2017, these wells were pumping out only 5,000 bbl. After gas injections began, the group produced 40,000 bbl a month—an average increase from about 15 B/D to 117 B/D per well. Another case involved 14 wells that peaked at 330,000 bbl a month in 2013, then dropped to 10,000 bbl. Post injection, output increased to 170,000 bbl a month. Watson’s report covers more than two dozen other shale EOR projects, though most lacked production results, revealing only project cost estimates. As opaque as the shale EOR effort has been thus far—at least outside of academic research—operators have shared these eye-openers for one simple reason: they have to. That is, if they want to receive the tax credits eligible for all EOR projects.

Preventing frac hits and well interferences with fast marching simulation using embedded discrete fracture models constrained by poroelastic geomechanical modelling of enhanced permeability

Investors are not happy. A recent article by a major newspaper (Olson et al., 2019) again highlighted the issue of ‘underperforming’ unconventional wells’ production targets. It notes that financial executives from the oil industry state that predicting well performance is an art rather than a science. Others involved in the management of billions of shale assets offer their opinion by noticing that ‘Geology doesn’t line up with Excel spreadsheets too well, unfortunately.’ These sentiments reflect the perceived state of unconventionals’ technology which is reduced to ‘an art and relying on Excel spreadsheets’. Such comments have dramatic consequences on all stakeholders in the unconventional business. A serious consequence came about shortly after the newspaper article was published: a law firm started an investigation targeting executives of an oil company accused of misleading their investors by providing them with false well performance forecasts. Lawyers and financiers quarreling about such highly technical matters put the burden on scientists and engineers to bring practical solutions to this complex problem. During this decade, great efforts have been made to better understand the performance of unconventional wells, but the challenges are overwhelming, and they evolve as our understanding of hydraulic fracturing progresses. Chief among these challenges, we find the issue of frac hits and well interferences.

E&P Notes (September 2018)

E&P Notes (September 2018)

New approach for predicting multiple fractured horizontal wells performance in tight reservoirs

Multiple fractured horizontal wells (MFHWs) are considered as the most effective stimulation technique to improve recovery from low permeability reservoirs particularly tight and shale assets. Understanding of the complex flow behaviour and predicting Productivity Index (PI) of these wells are vital for exploitation of unconventional reservoirs.The analytical or semi analytical models previously proposed for their PI calculations cannot accurately describe the flow behaviour around MFHWs mainly due to lack of capturing the complexity of the flow especially the fracture-to-fracture interference effects. Many of them are also too complex and/or not general enough limiting their use. The fine grid three-dimensional (3D) simulation approach is also costly and cumbersome. In this work, we followed a new approach to develop a new equation that can predict MFHWs performance under pseudo-steady state (PSS) flow conditions in tight reservoirs.A programming code, producing the include files, was coupled with a fine grid 3D commercial reservoir simulator to generate a large data bank. For these simulations, the pertinent parameters (matrix permeability, the number of fractures and fracture permeability, spacing, width, length and conductivity) were varied over wide practical ranges based on the Latin Hypercube sampling method.The individual impact of the parameters on PI, as the output variable, was evaluated by a statistical analysis technique under different prevailing conditions. It is shown, for instance, that increasing the fracture width and permeability does not result in a significant monotonic increase in PI while changing fracture length, spacing and numbers influence PI greatly.A new equation is then proposed that relates MFHWs-PI to a limited number of parameters by applying symbolic regression technique. Here, the total productivity index of MFHWs is related to the PI of the horizontal well with a single fracture, the number of fractures and dimensionless fracture spacing. The cross-validation results show that the proposed equation is general, reliable and simple for prediction purposes because it benefits from limited and appropriate dimensionless numbers with good values of fitting indices.This study expands our understanding of flow behaviour in tight reservoirs and provides an invaluable engineering tool that can facilitate simulation of flow around MFHWs, their optimum design and their well performance prediction.

Determining the main drivers in hydrocarbon production from shale using advanced data-driven analytics – A case study in Marcellus shale

Is it the quality of the formation or the quality of the completion that determines or controls the productivity of a shale well? In this paper we attempt to address this important question. We present a case study using a fit-for-purpose approach with no attempt to generalize the final conclusions. The analysis presented in this article is based on field measurements. No assumptions are made regarding the physics of the storage and/or the transport phenomena in shale. Our objective is to let the data speak for itself.The case study includes a large number of wells in a Marcellus shale asset in the northeast of the United States. Characteristics such as net thickness, porosity, water saturation, and TOC are used to qualitatively classify the formations surounding each well. Furthermore, wells are classified based on their productivity. We examine the hypothesis that reservoir quality has a positive correlation with the well productivity (wells completed in shale with better reservoir quality will demonstrate better productivity). The data from the field will either confirm or dispute this hypothesis.If confirmed, then it may be concluded that completion practices have not harmed the productivity and are, in general, in harmony with the reservoir characteristics. The next step in the analysis is to determine the dominant trends in the completion and judge them as best practices. However, if and when the hypothesis is disproved (wells completed in shale with better reservoir quality will NOT demonstrate better productivity), one can and should conclude that completion practices are the main culprit for the lack of better production from better quality shale. In this case, analysis of the dominant trends in the completion practices should be regarded as identifying the practices that need to be modified.Results of this study show that production from shale challenges many of our preconceived notions. It shows that the impact of completion practices in low quality shale are quite different from those of higher quality shale. In other words, completion practices that results in good production in low quality shale are not necessarily just as good for higher quality shale. Results of this study will clearly demonstrate that when it comes to completion practices in shale, “One-Size-fit-All” is a poor prescription.

Reconciling flowback and production data: A novel history matching approach for liquid rich shale wells

The recent downturn in petroleum prices poses a challenge to the oil and gas industry and in particular to the development of shale assets. This unfavorable economic situation calls for improved reservoir characterization and modeling that are necessary for the optimization of well design and cost. Current rate transient analysis (RTA) techniques are well-suited for the reservoir characterization of dry shale gas wells. However, they have limited accuracy for the more profitable liquid rich shale (LRS) wells. For example, RTA does not accurately model three-phase flow of gas, water, and condensate that can occur in the reservoir. Further, application of RTA typically ignores post-fracturing water flowback data that can be analyzed for important information pertaining to hydraulic and secondary fractures. In this work, the reservoir of LRS wells is characterized by simulating and history matching the three-phase flow of early flowback and long-term production.In this work, characterization and history matching are performed by utilizing a novel procedure that includes an equation of state (EOS), a compositional reservoir simulator, and a multi-objective optimization (MOO) algorithm. In addition, the simulation approach incorporates various water trapping mechanisms that are necessary to reproduce the water production rate trends. The procedure minimizes the misfit between observed and simulated production data. It starts by first tuning the EOS to a surface recombined sample. Then, a triple-porosity (i.e., hydraulic fracture, secondary/natural fracture, and matrix porosities) simulation model is applied that utilizes the multiple interacting continua (MINC) method and accounts for permeability jail, pressure-dependent permeability, capillary pressure, and gravity segregation. Finally, an MOO algorithm is used, namely the fast nondominant sorting genetic algorithm (NSGA-II), to solve the misfit minimization problem.The introduced procedure has a number of advantages. One advantage is the simultaneous matching of flowback and production data which improves rock and fracture characterization. Another advantage is the utilization of MINC combined with logarithmic gridding that can model transient flow in a triple porosity system. Moreover, it can model different fracture shapes by adjusting a shape factor parameter. In addition to these, the use of MOO does not require predetermined weights unlike the aggregate function approach. In this article, detailed description of all the steps of the history matching workflow are presented, and its applicability demonstrated with a field example from the Montney Formation.

Optimization of hydraulic fracturing design under spatially variable shale fracability

Abstract Current hydraulic fracturing designs are based on equal fracture intervals (spacing) along horizontal wells. Typical fracture spacing ranges from 50 to 600 feet while the fracture half-lengths normally vary between 100 and 600 feet. Symmetric spacing designs assume homogeneous shale mechanical property distributions, e.g. rock brittleness or fracability, which may not be the case in many shale plays. Fracability of the shale rock is an important property that controls the efficiency of the fracturing process. Therefore, the heterogeneity in shale fracability can have an important impact on the design and optimization of the hydraulic fracturing process. In particular, the optimal number, location, and length of the fractures can depend on rock fracability distribution. In this paper, we develop an optimization approach for hydraulic fracturing design under geospatial variability in shale fracability and investigate several aspects of the proposed fracture design optimization approach. In particular, we optimize the hydraulic fracturing design by implementing a variant of the Simultaneous Perturbation Stochastic Approximation algorithm to maximize the net present value of the shale asset, including the cost of fracturing and the revenue from gas production. The optimization framework provides a systematic design approach for hydraulic fracturing of unconventional reservoirs and can be applied to improve production efficiency and reduce the cost and environmental impact of hydraulic fracturing. We demonstrate the effectiveness and suitability of the proposed method using a series of numerical experiments in shale gas development.

Full field reservoir modeling of shale assets using advanced data-driven analytics

Hydrocarbon production from shale has attracted much attention in the recent years. When applied to this prolific and hydrocarbon rich resource plays, our understanding of the complexities of the flow mechanism (sorption process and flow behavior in complex fracture systems - induced or natural) leaves much to be desired. In this paper, we present and discuss a novel approach to modeling, history matching of hydrocarbon production from a Marcellus shale asset in southwestern Pennsylvania using advanced data mining, pattern recognition and machine learning technologies. In this new approach instead of imposing our understanding of the flow mechanism, the impact of multi-stage hydraulic fractures, and the production process on the reservoir model, we allow the production history, well log, completion and hydraulic fracturing data to guide our model and determine its behavior. The uniqueness of this technology is that it incorporates the so-called “hard data” directly into the reservoir model, so that the model can be used to optimize the hydraulic fracture process. The “hard data” refers to field measurements during the hydraulic fracturing process such as fluid and proppant type and amount, injection pressure and rate as well as proppant concentration. This novel approach contrasts with the current industry focus on the use of “soft data” (non-measured, interpretive data such as frac length, width, height and conductivity) in the reservoir models. The study focuses on a Marcellus shale asset that includes 135 wells with multiple pads, different landing targets, well length and reservoir properties. The full field history matching process was successfully completed using this data driven approach thus capturing the production behavior with acceptable accuracy for individual wells and for the entire asset.

The importance of the formation and fracture pressures for the selection of the depths for casing setting in Slovakia

The successful development of shale gas requires a fundamental understanding of shales and that can be challenging because these rocks vary significantly. For recoverable shale gas resources assessment a broad range of geological, geochemical, geophysical and geomechanical data is required. Information about the variable lithologies, mineralogies, and kerogen content is vital to locate favorable intervals for gas production. Knowledge of the in-situ stresses and porosity of these intervals is essential for developing hydraulic fracturing strategies to recover the gas in place. Geomechanical properties of shales are important in terms of determination of the likelihood of fractures initiating and propagating in shale materials. Geomechanical properties of rock include: Poisson's Ratio, Total Minimum Horizontal Stress, Bulk, Young and Shear Modulus. These properties play significant role in developing shale assets. Having access to geomechanical data can assist engineers and geoscientists during geomechanical modeling, hydraulic fracture treatment design and in some cases during reservoir modeling in shale gas fields. This paper is intended to give characterization of geomechanical properties of shale rock. It then refers to a case study of Baltic Basin of shale gas in Poland, for discussing it geological condition and rock properties.

Geomechanical Properties of Unconventional Shale Reservoirs

Production from unconventional reservoirs has gained an increased attention among operators in North America during past years and is believed to secure the energy demand for next decades. Economic production from unconventional reservoirs is mainly attributed to realizing the complexities and key fundamentals of reservoir formation properties. Geomechanical well logs (including well logs such as total minimum horizontal stress, Poisson’s ratio, and Young, shear, and bulk modulus) are secured source to obtain these substantial shale rock properties. However, running these geomechanical well logs for the entire asset is not a common practice that is associated with the cost of obtaining these well logs. In this study, synthetic geomechanical well logs for a Marcellus shale asset located in southern Pennsylvania are generated using data-driven modeling. Full-field geomechanical distributions (map and volumes) of this asset for five geomechanical properties are also created using general geostatistical methods coupled with data-driven modeling. The results showed that synthetic geomechanical well logs and real field logs fall into each other when the input dataset has not seen the real field well logs. Geomechanical distributions of the Marcellus shale improved significantly when full-field data is incorporated in the geostatistical calculations.

A Critical View of the Current State of Reservoir Modeling of Shale Assets

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 165713, ’A Critical View of Current State of Reservoir Modeling of Shale Assets,’ by Shahab D. Mohaghegh, SPE, Intelligent Solutions and West Virginia University, prepared for the 2013 SPE Eastern Regional Meeting, Pittsburgh, Pennsylvania, USA, 20-22 August. The paper has not been peer reviewed. The coupling of hydraulic fractures and natural-fracture networks and their interaction with the shale matrix remains a major challenge in reservoir simulation and modeling of shale formations. This article reviews methods used to understand the complexities associated with production from shale to shed light on the belief that there is much to be learned about this complex resource and that the best days of understanding and modeling how oil and gas are produced from shale are still ahead. Introduction Preshale Technology. The phrase “preshale” technology aims to emphasize the combination of technologies that are used to address the reservoir and production modeling of shale assets. In essence, almost all of the technologies used today for modeling and analysis of hydrocarbon production from shale were developed to address issues that originally had nothing to do with shale. As the shale boom began, these technologies were revisited and modified in order to find application in shale. Conventional Discrete Fracture Network. The most common technique for modeling a discrete natural fracture (DNF) network is to generate it stochastically. The common practice in carbonate and some clastic rocks is to use borehole-image logs to characterize the DNF at the wellbore level. These estimates of DNF characteristics are then used for the stochastic generation of the DNF throughout the reservoir. The idea of the DNF is not new. It has been around for decades. Carbonate rocks and some clastic rocks are known to have networks of natural fractures. Developing algorithms and techniques to generate DNFs stochastically and then couple them with reservoir-simulation models was common practice before the so-called “shale revolution.” A New Hypothesis on Natural Fractures in Shale What are the general shapes and structures of natural fractures in shale? Are they close to those of the stochastically generated set of natural fractures with random shapes that has been used for carbonate (and sometimes clastic) formations? Or are they more like a well-structured and well-behaved network of fractures that have a laminar, plate-like form, examples of which can be seen in outcrops (such as those shown in Fig. 1)? Shale is defined as a fine-grained sedimentary rock that forms from the compaction of silt and clay-sized mineral particles commonly called mud. This composition places shale in a category of sedimentary rocks known as mudstones. Shale is distinguished from other mudstones because it is fissile and laminated.

Technology Focus: Reservoir Simulation (July 2014)

Technology Focus As the development of shale oil and gas becomes increasingly significant, so does the need for modeling tools for their accurate and timely forecasting. The basic question then arises: Are the simulation tools that we have suitable for the job? Can they explain, and ultimately forecast, the contribution to flow from natural or hydraulically induced fractures? Ultimately, we need to be confident that the results from such models are sufficiently reliable for decision-making purposes. As Alexandre Emerick stated in JPT (April 2014), “The goal is to generate models for production forecasting aiding the decision-making process involved in the development and management of petroleum reservoirs.” The ability to model this increasingly important asset class is one of the primary issues facing reservoir and simulation engineers today. Our existing arsenal of simulation tools is certainly time-tested for conventional assets, but can they deliver reliable forecasts for this emergent class? Recent literature is rich with relevant studies ranging from fundamental laboratory experiments to entirely empirical, data-driven, approaches for predictive reservoir management. These articles raise numerous questions, including the possible oversimplification of a complex problem, the very nature of the production mechanisms, and the role played by any pre-existing discrete fracture networks. Additional concerns relating to accuracy and reliability of the underlying data used to populate such models have also been raised. Keeping an open mind on this topic is, I feel, appropriate. One needs to balance the very real need for such predictive tools today with the recognition that future evidence may disrupt certain preconceptions. To this end, four articles have been selected that span a spectrum of ideas. One considers practical applications and solutions, while another covers the emerging technique of molecular simulation. Another eloquently probes various notions in our current state of understanding of shale assets and examines issues in interpreting the forecasts furnished by our simulation models. The fourth article demonstrates some creative thinking by representing a planar fracture by use of a proxy comprising a coupled flowing network model, a concept that may prove flexible and warrants further investigation. Conventional modeling tools used to predict flow in tight, fractured shales (e.g., dual-porosity models, local grid refinements, tartan grids) may, indeed, provide reliable forecasting. Analytic and data-driven approaches are also acceptable in some circumstances. However, as our knowledge in exploiting these assets deepens with new theory, laboratory experiments, and field and operational experience, we should be prepared to reconsider some of today’s axioms in the future.