Layer | Fill | Outline |
---|
Map layers
Theme | Visible | Selectable | Appearance | Zoom Range (now: 0) |
---|
Fill | Stroke |
---|---|
Collaborating Authors
Baker Atlas
The Direct Measurement of Carbon in Wells Containing Oil and Natural Gas Using a Pulsed Neutron Mineralogy Tool
Pemper, Richard R. (Baker Atlas) | Han, Xiaogang (Baker Atlas) | Mendez, Freddy Enrique (Baker Hughes) | Jacobi, David (Baker Hughes) | LeCompte, Brian (INTEQ) | Bratovich, Matt (Baker Atlas) | Feuerbacher, Gary (INTEQ) | Bruner, Mike (Baker Hughes) | Bliven, Steve (Baker Hughes)
Abstract The assessment of reservoir productivity and subsurface hydrocarbon can be significantly enhanced through an understanding of formation mineralogy and organic carbon. Such information allows petrophysicists to resolve ambiguities in their predictions of reservoir hydrocarbon potential. While core samples are a prime source for exact formation mineralogy, excellent results can also be derived in a timely and cost-efficient manner from in-situ log chemistry measurements of the rock. A direct measurement of the formation's elemental concentrations is achieved using a gamma ray scintillation sensor in combination with a 14-MeV pulsed-neutron generator. The most important element measured is carbon, as it may provide a direct indication of reservoir hydrocarbons. This paper presents a method for determining the amount of organic carbon in subsurface formations using a pulsed-neutron mineralogy tool and a natural gamma ray spectroscopy tool. The natural, inelastic, and capture gamma ray energy spectra from these instruments are used to extract the chemistry of the subsurface formation being investigated. The elemental concentrations measured include Al, C, Ca, Fe, Gd, K, Mg, S, Si, Th, Ti, and U. Carbon is very difficult to measure without the inelastic spectrum generated from a pulsed-neutron source. An interpretation process, based upon the geochemistry of petroleum-bearing formations, is employed to derive the lithology and mineralogy which leads to the interpretation of the carbon measurement. The oil saturation can be computed in conventional reservoirs, assuming that the amount of carbon in excess of that required for the inorganic matrix mineralogy is part of the pore fluid as hydrocarbon. The direct carbon measurement is also important in laminated shaly sands or in low-salinity reservoirs, where oil saturation determination from indirect measurements, such as resistivity, is not compatible with the environment. In other formations the carbon can be determined to be a component of the rock matrix as kerogen or coal, both of which are uniquely identified with this logging system. Kerogen becomes extremely important in the evaluation of shale gas formations. Field examples are presented to illustrate the effectiveness of the carbon measurement. Introduction Subsurface organic carbon, i.e., carbon that does not belong to any of the carbonate minerals, indicates the presence of oil, natural gas, coal, or kerogen. Although the amount of carbon is one of the most important quantities in formation evaluation, openhole tools often provide only indirect measurements of hydrocarbons. Traditional electrical tools, for example, measure oil saturation indirectly based upon a comparison of the resistivity of saline and non-saline formation fluids. This approach works best when the salinity of the formation water is high to moderate; if connate water salinity is low, resistivity methods cannot differentiate water from hydrocarbon.
- Geology > Geological Subdiscipline > Mineralogy (1.00)
- Geology > Geological Subdiscipline > Geochemistry (1.00)
- Geology > Petroleum Play Type > Unconventional Play (0.93)
- (2 more...)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
Effective Geochemical and Geomechanical Characterization of Shale Gas Reservoirs From the Wellbore Environment: Caney and the Woodford Shale
Jacobi, David J. (Baker Hughes) | Breig, John J. (Newfield Exploration) | LeCompte, Brian (INTEQ) | Kopal, Margarete (Baker Hughes INTEQ) | Hursan, Gabor (Baker Atlas) | Mendez, Freddy Enrique (Baker Hughes) | Bliven, Steve (Baker Hughes) | Longo, John
Abstract The successful recovery of hydrocarbons from gas shales requires a fundamental understanding of the reservoir's rock-matrix properties. 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. Often these properties are established from the analysis of cores extracted from the wellbore, a time-consuming practice which causes costly delays in well completions and prolonged rig time. We demonstrate that these reservoir rock properties can be measured and predicted in-situ from the wellbore environment by a formation evaluation method that employs a combination of measurements made by downhole geochemical, acoustic, and nuclear magnetic resonance sondes. Using this combination of tool measurements we determine lithology, mineralogy, and kerogen content. The mineralogy, porosity, acoustic velocities, bulk density, pore pressure, and overburden stress are then used to compute the unconfined compressive strength, Poisson's ratio, and horizontal stress for each interval. These results can then be used to develop hydraulic fracture strategies. The effectiveness of this approach is shown through characterization of the rock properties of the Caney and the Woodford Shale from three different wells. The ability to quantify the kerogen content from these formations is emphasized as there is currently no other direct quantification of carbon from openhole wireline logging available. This approach for characterization of shale gas reservoirs is also further supported through comparisons of core data that display the mineralogy, chemistry, kerogen content, and geomechanical properties from the wellbore section. Introduction The Woodford and Caney formations comprise a successive series of fissile, carbonaceous, siliceous black shales that are unconventional, economic gas plays in the Arkoma Basin of eastern Oklahoma (Amsden, 1967; Cardot, 1989, Brinkerhoff, 2007, Schad, 2007). Producing commercial gas from these fine grained lithologies requires the stimulation of a large volume of rock using hydraulic fracture techniques. The projected azimuth, propagation, and containment of the induced fractures created using this method are sometimes difficult to predict. Fracture growth is impeded when stimulation stages do not successfully target siliceous lithofacies with lower fracture gradient. These can often induce extensive intersecting fractures or contain dormant mineralized fractures that upon reactivation may increase production. Instead, some stages are inadvertently applied to argillaceous zones that attenuate fracture development due to embedment. Other stages may be directed toward carbonate facies that have high breakdown pressures. Treatment pressures simply are unable to exceed the fracture gradient of the rock. Stimulations may also propagate along fault planes intersecting other formations within the basin leaving much of the reservoir rock unfractured (Vulgamore et al., 2007). Because of these problems, there can be uncertainty about whether there has been fracture containment within the zone of interest or whether optimal zones that promote gas recovery have indeed been fully accessed. For example, induced fractures into the Woodford can pose questions of whether these have been contained within the target area or have grown upward into the overlying Caney or downward into the underlying Hunton limestone. The differences in geochemical, petrophysical and geomechanical properties of the lithofacies found in both the Caney and Woodford can be used to improve hydraulic fracture strategies. Using a combination of logging tool measurements, we determine the kerogen content, porosity, mineralogy, and the principal stresses of the various lithofacies from the wellbore environment for three wells. Results will show how the integration of these into a petrophysical model provides reservoir characterization properties comparable to those gained from core analysis, which has the potential to save money and expedite well completions.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
Abstract In order to ensure well stability, distinguish high- and low- pressure zones and estimate the level of pressure depletion, information about formation pressure is necessary. Due to formation damage during drilling and mud filtrate invasion, true formation pressure cannot be measured directly when formation permeability is relatively small. Therefore, an accurate model of invasion profile is required to calculate true formation pressure from formation testing data. This is possible to achieve by combining drilling with LWD and/or wireline logging data. This paper describes a method of computing depth of invasion by inversion of resistivity logging data. We use resistivity image data to calculate flushed zone resistivity and induction logging data to compute true formation resistivity. This, in turn, provides an invasion zone profile and significantly reduces the ambiguity of possible solutions. Drilling regime, rate of penetration, wellhead pressure, and mud properties are used to calculate wellbore pressure. The changes in formation pressure during drilling are computed by the hydrodynamic model of invasion. We present the result of formation testing data processing for water-saturated reservoir. The true formation pressure is estimated using the results of inversion, namely, estimate of mud filtrate volume penetrated into formation. Drilling Mud Invasion During drilling, the pressure overbalance is created to provide well stability and prevent blow-outs. Due to this pressure difference a certain volume of drilling mud filtrate invades the formation. The composition of filtrate is different from that of the formation fluid. Depending on filtration resistance and time of action the depth of invaded zone can vary from a few centimeters to 0.8–1.0 m. Thus, logging measurements with relatively shallow depth of investigation (less than invasion depth) provide information about the disturbed part of the formation and not about the virgin formation parameters. In order to improve interpretation of logging data, it is important to be able to estimate rate of change of formation parameters during drilling. In this paper we present a method to estimate total volume of mud filtrate penetrating into formation. The results are used to improve interpretation of formation testing data. A method to evaluate mud invasion characteristics from resistivity logging data was suggested in Kashevarov et al. (2003). One of the ideas of the method is the change in salt concentration profile due to invasion. It is caused by different salinities of mud and formation fluids and changes in salt concentration and saturation profiles during invasion. This leads to the changes in the resistivity profile. Information about the latter is obtained using resistivity logging data acquired by tools with different depths of investigation. In this paper we used induction and micro-resistivity logging data to calculate the resistivity profile in the near-wellbore zone. The micro-resistivity tool has a very shallow depth of investigation and is therefore sensitive to the flushed zone, which is the nearest to the borehole. Induction logging has much larger depth of investigation and is used mainly to measure true formation resistivity. We use data acquired by both tools simultaneously to define general geoelectric model of the near-wellbore zone. By integrating the two resistivity logging methods we obtain more reliable results and a more detailed resistivity profile.
Abstract Fracture corridors are sub-vertical tabular fracture clusters which are associated with faults, or originate as incipient faults. Fluid conductive fracture corridors may have significant permeability and profoundly affect reservoir flow dynamics. Mapping fracture corridors requires (i) locating fracture corridors and assigning to them (ii) length, (iii) orientation and (iv) fluid conductivity. Estimation of orientation and especially length is a major challenge in fracture corridor modelling. Exclusion zones may aid both in locating fracture corridors and assigning orientation and length. An exclusion zone is an area which cannot have a conductive fault or fracture corridor passing through. Exclusion zones around vertical wells are defined mainly by well tests and remaining oil column thickness. Borehole image logs, wells tests, lost circulation and openhole logs define exclusion zones in horizontal wells. A vertical well test with homogeneous matrix cannot have a fluid conductive fault / fracture corridors within the circle of investigation. Oil column thickness can be estimated from a combination production logs, water cut history or from recent openhole or surveillance logs. Wells with thick remaining oil column and no water sweep cannot have a nearby fluid conductive fault or fracture corridor. Horizontal wells with image logs or openhole logs showing no conductive fault/fracture corridor indicate an exclusion zone around the well where no faults or fracture corridors can pass through. Introduction Characterizing fractured reservoirs involves modeling two main types of fractures:layer-bound tensile fractures fracture corridors (Fig. 1). Layer- bound fractures are controlled by mechanical layer properties such as thickness, porosity, dolomite and shale percent. Modeling layer-bound fractures, therefore, puts main emphasis on identifying mechanical layers. Fracture corridors, on the other hand, are sub-vertical tabular fracture clusters which often intersect the entire reservoir regardless of the layering. Fracture corridors are either associated with faults or originate as incipient faults with little or no visible displacement. Fracture corridors may extend laterally for several tens or hundreds of meters and have a wide spectrum of size and fluid conductivity. Fracture conductivity depends mainly on aperture and density. However, a system of conductive fractures can have permeability only if the fractures are interconnected. Fracture connectivity is determined by fracture density, average length and angular scatter. Fracture corridors often include small and large aperture fractures (mega-fractures) which are clustered within a narrow zone with a high degree of connectivity and extremely high fluid conductivity parallel to the strike of the fracture corridor. For this reason, it is very important to map fracture corridors deterministically from available static and dynamic data for field development, well planning and reservoir simulation purposes. Deterministic mapping of fracture corridors requires finding (i) location ii) estimating length and (ii) orientation. It is often impossible to capture all fracture corridors and all three attributes from a single source of data. Fracture corridor mapping has to rely on integrating data from different sources which include seismic data, borehole image logs, openhole logs, production logs, production history data and well tests (Ozkaya and Richard 2006). Unfortunately, even with a large spectrum of data it is not always possible to pin down all fracture corridors or determine their length or orientation. In this paper we discuss how the concept of exclusion zones can be used to constrain fracture corridor occurrence and estimate orientation and length.
- Geology > Geological Subdiscipline > Geomechanics (0.68)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.34)
- Geophysics > Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
Summary This paper describes the procedure of building a probabilistic decision tree on the basis of the integration of data from multiple sources, conditional probabilities, and the application to map fracture corridors (FCs) in a mature oil field with abundant production data. A fracture corridor is a tabular, subvertical, fault-related fracture swarm that intersects the entire reservoir and extends laterally for several tens or hundreds of meters. Direct indicators of fracture corridors, such as image logs, flow profiles, well tests, and seismic fault maps, are sometimes insufficient to map all fracture corridors in a field. It is also necessary to use indirect fracture-corridor indicators from well data, such as productivity index (PI), gross rate, water cut, and openhole logs. Fracture corridors from indirect indicators can be inferred by a probabilistic decision tree, which makes predictions by integrating data from multiple sources, giving preference to the indicators with the highest relevance. Decision trees are constructed by use of a training set that includes measurements of both direct and indirect fracture-corridor indicators. In this study, wells with borehole images, production logs (flow profiles), and injector/producer short cuts are selected as the training set. The resulting decision trees reveal that total losses, gross production rates, and water cuts are the three most effective indirect indicators of fracture corridors in the test field. Introduction It is often the case that a particular reservoir attribute, such as porosity, has only sparse direct measurements. It is possible, however, to predict values of such a target variable with the help of a set of other variables that exhibit some degree of correlation to the target variable and have abundant measurements. A common example is estimating porosity from seismic attributes. In this paper, the variables that have one-to-one correspondence to the target variable are called direct indicators and the variables that have some degree of correlation are called indirect variables. For example, density and neutron logs are direct indicators of porosity, whereas seismic impedance is an indirect indicator. There are several statistical techniques to predict a target variable from a set of indirect indicators, and these can be collected under two main groups: supervised prediction techniques and unsupervised prediction techniques. In the case of supervised prediction techniques, indirect indicators are correlated to a target variable by use of a training set of data that includes measurement of both direct and indirect indicators of the target variable. The generated predictive system can be used to estimate values of the target variable solely on the basis of indirect indicators in wells that do not have any measurement of direct indicators. Multiple regression, back propagation, neural networks, and Bayesian decision trees belong to this category. In cases where the training set is small or no direct indicators are available, it is possible to adopt statistical techniques that do not require extrapolation from a training set. These are termed unsupervised prediction techniques. Several such techniques exist, including cluster analysis, unsupervised neural networks, and factor analysis (Wasserman 1989; Chester 1993; Van De Geer 1971). The basic idea is to discover hidden factors that control indicator variables and to interpret these factors in terms of the target variable. For example, the density (spacing/relative abundance) of conductive fractures may affect the rapid water-cut rise, high initial PI, and high gross rate. These three indirect indicators will be highly correlated to each other. An unsupervised prediction technique may uncover the hidden factor (fracture density) that controls all three variables from the high correlation among them. Both supervised and unsupervised inferences are methods for making predictions with incomplete information (Tamhane et al. 2000; Fletcher and Davis 2002). Most of the applications in the oil industry use fuzzy logic or fuzzy neural networks. These applications also use soft computing decision making with incomplete evidence and risk reduction by use of a fuzzy-expert system (Weiss et al. 2001; Chen et al. 2002; Saggaf and Nebrija 2003). This idea has found some application, especially in mapping fracture density by use of seismic attributes (Ouenes et al. 1995; Zellou et al. 2003; Bloch et al. 2003). Both supervised and unsupervised statistical techniques aim at determining some global attribute of dispersed fractures, such as density. It is often fracture corridors, however, rather than dispersed fractures that are characterized as the main reservoir heterogeneity (Ozkaya and Richard 2006). An FC is a tabular, subvertical, fault-related fracture swarm that intersects the entire reservoir and extends laterally for several tens or hundreds of meters (Fig. 1). FCs could be fluid-conductive or cemented. In this paper, an FC denotes a fluid-conductive FC unless otherwise specified. FCs may have significant conductivity and may play a major role in reservoir dynamics by providing pressure support and, therefore, causing early water breakthroughs and increased gross rates. The four main requirements to map an FC are location, strike, length, and conductivity. Here, we focus primarily on locating FCs and discuss only briefly how other attributes can be estimated. Our objective is not the actual mapping of FCs but examining Bayesian decision trees as a viable technique in FC identification. The basis and procedures for calculating conditional probabilities, entropy, information Gain (IG), and the construction of decision trees are explained in the Appendix.
- Asia (1.00)
- North America > United States > Texas (0.68)
- North America > United States > California (0.46)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (0.54)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (21 more...)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Faults and fracture characterization (1.00)
- Management > Risk Management and Decision-Making > Decision-making processes (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Fuzzy Logic (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Diagnosis (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Decision Tree Learning (1.00)
Abstract This case study illustrates a critically stressed fracture analysis (CSFA) for three wells drilled in offshore Vietnam fractured granites. A CSFA identifies fractures which are optimally aligned to fail in the present-day stress regime, and are most likely to be conduits to fluid flow. Identifying such fracture orientations is essential for optimal wellbore placement and drilling trajectory. The fracture dip angle and azimuth were manually extracted from electrical and acoustic borehole images. Four pressure and in-situ stress support cases were analyzed and plotted on Mohr-Coulomb diagrams. In the case of high-pressure fractures and efficient in-situ stress support, two fracture sets of possible critically stressed fractures are identified in stereograph plots: striking circa N-S and dipping either to the East or to West with high dip angles around 75 degrees, and striking NNW-SSE and dipping either to the ENE or WSW with dip angles around 75 degrees. This CSFA shows that cemented and non-permeable fractures (non-hydraulically conductive) are not under a critical shear state for the in-situ stress tensor obtained in the field. However, critically stressed fractures can exist in the basement formation when they are hydraulically connected (open fractures), and highly pressurized, providing a Biot equal to 1. The optimum drilling trajectory that would intersect more CSF would be high-angle wells in the direction of NE-SW or W-E. A production logging tool (PLT) run in the follow up Well-4 confirms the results. In the case of fractured granites it is critical to maximize formation evaluation information from the first few wells to evaluate the field potential before too much money is spent on appraising what could be an uneconomic reservoir. CSFA is an essential step for maximizing this information. Introduction This case study is based on Hoan Vu JOC (Joint Operating Company) data acquired in the naturally fractured granitic basement of the Cuu Long Basin, Vietnam, located about 150 km offshore from Vung Tau city. The critically stressed fracture study was carried out from wireline, simultaneously acquired acoustic and resistivity borehole image logs in three exploratory wells; Well-1, Well-2, and Well-3. The wells were drilled over 2002–2006 and the CSFA study was completed in the first quarter of 2007. The recommendation of optimum drilling trajectories towards productive fractures in the basement from the results of the critically stressed fracture study was confirmed from the PLT run in the Well-4.
- North America > United States > Texas (0.28)
- Asia > Vietnam > South China Sea (0.25)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Igneous Rock > Granite (0.45)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (0.89)
Abstract The confidence of stakeholders in corporations has been shaken by a series of corporate wrongdoings. Over the past several years, numerous top executives have been found guilty of improprieties such as illegal accounting methods and violating the Foreign Corrupt Practices Act (FCPA) and other anti-corruption statutes. As a result, government authorities in the U.S. and other countries have been expanding their oversight of the ways organizations manage their businesses and the records they generate. Failing to comply with these standards can have significant consequences with the possibility of fines, other monetary penalties, jail time for executives, adverse share price impact and loss of market share. Legal compliance is a challenging and essential element of corporate governance. Companies pursuing opportunities in developing economies often face additional hurdles in fashioning an effective compliance program due to unfamiliar geographies, numerous complex local laws, cultural barriers, and political and financial complexities. Improper business conduct in such locations can expose a company to unforeseen risk, reputational damage and civil and criminal penalties. As a result, organizations should work to ensure that their operations meet the highest standards of both ethical behavior and legal compliance. In the current economic, regulatory and social climate, compliance risks have propelled integrity, good corporate governance and effective and efficient compliance management to a top business priority. Baker Hughes Incorporated (BHI) firmly believes that ethics is essential for sustained high business performance. To ensure that it meets these challenges, BHI has developed a global best-in-class compliance program that can serve as a model for other companies facing formidable global compliance challenges. BHI's compliance program consists of a rigorous, customized system of corporate governance. The program requires all employees to comply with the law, the company's core values—integrity, teamwork, performance and learning—and its business code of conduct. BHI's compliance program has a special focus on legal compliance, financial compliance, and health, safety and environmental compliance, all of which are well defined and articulated through every part of the company. The compliance culture that has evolved at BHI is based on the critical concepts of prevention, detection and response. In terms of specifics, BHI's compliance program: establishes proper tone at the top; assigns oversight and management responsibility for the compliance program to a vice president and chief compliance officer with active oversight from executive management and the board of directors; establishes clear global compliance standards and procedures; assigns compliance responsibility and accountability to line managers and all employees; provides mandatory, customized training at all levels; provides a robust and easily accessible reporting system for compliance violations to employees and other stakeholders; and manages and investigates all allegations of misconduct and noncompliance in a timely and competent fashion. A key component of the company's compliance program is FCPA compliance. This paper outlines effective processes and procedures that have been employed by BHI, both from a compliance as well as commercial perspective, which can be successfully utilized for managing complex international oil and gas businesses, while fulfilling the obligations of a good corporate citizen.
- Law > Criminal Law (1.00)
- Law > Business Law (1.00)
- Government > Regional Government > North America Government > United States Government (1.00)
- Energy > Oil & Gas (1.00)
Integrated Petrophysical Evaluation of Shale Gas Reservoirs
Jacobi, David J. (Baker Hughes) | Gladkikh, Mikhail (Baker Hughes Inc) | LeCompte, Brian (INTEQ) | Hursan, Gabor (Baker Atlas) | Mendez, Freddy (Baker Hughes) | Longo, John | Ong, Seehong (Baker Atlas) | Bratovich, Matt (Baker Atlas) | Patton, George Larry (Baker Hughes) | Shoemaker, Phillip (Shoemaker Exploration Company)
Abstract Gas shales are economically viable hydrocarbon prospects that have proven to be successful in North America. Unlike conventional hydrocarbon prospects, gas shales serve as the source, seal, and the reservoir rock. Generating commercial production from these unique lithofacies requires stimulation through extensive hydraulic fracturing. The absence of an accurate petrophysical model for these unconventional plays makes the prediction of economic productivity and fracturing success risky. This paper presents an integrated approach to petrophysical evaluation of shale gas reservoirs, specifically, the Barnett Shale from the Fort Worth basin is used as an example. The approach makes use of different formation evaluation data, including density, neutron, acoustic, nuclear magnetic resonance, and geochemical logging data. This combination of logging measurements is used to provide lithology, stratigraphy and mineralogy. It also differentiates source rock intervals, classifies depositional facies by their petrophysical and geomechanical properties, and quantifies total organic carbon. The analysis is also employed to locate optimal completion intervals, zones preferable for horizontal sections, and intervals of possible fracture propagation attenuation. Resistivity image analysis complements the approach with the identification of natural and drilling induced fractures. We compare results from three different wells to show the effectiveness of the method for shale gas characterization. The methodology presented provides a means to understand the geomechanical and petrophysical properties of the Barnett Shale. This knowledge can be used to design a selective completion strategy that has the potential to reduce fracturing expenses and optimize well productivity. Though developed specifically for the Barnett Shale, the underlying ideas are applicable to other thermogenic shale gas plays in North America. Introduction Numerous organic-rich shale sections located in some North American basins have been proven as productive natural gas plays (Jarvie et al., 2007; Martini et al., 2003; Pollastro et al., 2003; Pollastro et al., 2007; Pollastro, 2007). They extend over large geographical areas and offer sustainable reservoirs with attractive exploration and development costs (Hill and Nelson, 2000). Economic production from these complex, kerogen-rich formations, which typically possess poorly-defined gas-water contacts, natural fractures, and very low matrix permeability, depends heavily on the completion technology implemented for recovery. The primary strategy used for stimulating production is hydraulic fracturing, the scale of which can pose a major cost and challenge to producers (Mayerhofer et al., 2006). The challenge is mainly related to the difficulties involved in monitoring and predicting the propagation of the fracturing process through the strata in order to recover potential reserves (Le Calvez et al., 2006; Mayerhofer et al., 2006; Moore and Ramakrishnan, 2006). This uncertainty can be traced to the varying geomechanical properties associated with the complex lithofacies inherent in many shale gas sections. For this reason, shale gas lithofacies and their relation to reservoir stratigraphy and productivity has recently become a focus of producers (Bowker, 2007; Hickey and Henk, 2007). As a result, the ability to define and categorize in situ the complex lithofacies associated with shale gas plays according to kerogen content, mineralogy, and geomechanical properties has the potential to aid in reducing the costs involved in hydraulic fracturing and at the same time improve hydrocarbon recovery.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Passive Seismic Surveying > Microseismic Surveying (0.46)
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- North America > United States > Texas > Ardmore - Marieta Basin > Newark East Field > Barnett Shale Formation (0.99)
- North America > United States > Michigan > Michigan Basin > Wise Field > Dundee Limestone Formation (0.91)
Abstract Uncertainty in well test analysis results from errors in pressure and rate measurements, from uncertainties in basic well and reservoir parameters; from the quality of the match with the interpretation model; and from the non-uniqueness of the interpretation model. Yet, well test analysis results are usually reported as unique values, often with unrealistic precision. Most well test interpretation software programmes use non-linear regression to determine the reservoir parameters that provide the best match between actual rate and pressure data and a given interpretation model. The non-linear regression evaluates the match quality as a standard correlation between each parameter. This, however, only estimates the match errors, and does not incorporate the other errors that must be accounted for to understand the overall uncertainty on the analysis. The paper presents a practical methodology for the determination of error bounds in well test analysis and illustrates its application with well tests from an oil reservoir and a gas condensate reservoir in the North Sea in order to evaluate typical error bounds for the most common parameters such as permeability-thickness, permeability, skin effect, fracture, horizontal well lengths and distances to boundaries. Differences in error bounds between hand and computer analysis are also discussed. Introduction Uncertainty in well test analysis results from errors in pressure and rate measurements, from uncertainties in basic well and reservoir parameters; from the quality of the match with the interpretation model; and from the non-uniqueness of the interpretation model (Horne 1994). Yet, well test analysis results are usually reported as unique values, often with unrealistic precision. This has been exacerbated by the use of hand calculators and, later on, computers and well test interpretation software for performing well test analysis calculations, because engineers, students and professionals alike, seem to believe that because the tools they use display eight decimal places, all eight places are accurate. Consequently, distances to boundaries are often reported with a resolution of a tenth of a foot, skin with two decimal digits, and permeabilities greater than 100 mD within 0.1 mD, i.e. with resolutions better than 0.1%, which is utterly ridiculous.
- North America > United States > Texas (1.00)
- Europe > United Kingdom (0.66)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (22 more...)
Abstract A review of available saturation information to support an infill well target concluded that sufficient uncertainty existed to warrant further well interventions to acquire data supporting the proposed location. Mixed water salinity within the formation added to the complexity of surveillance. Under these conditions traditional sigma analysis can be easily confused while the logged intervals are long enough to effectively preclude the exclusive use of CO logging techniques. The data acquisition plan for the well intervention used a combination of limited CO logging and Sigma logging as well as a trial of a new pulse neutron saturation determination technique (Fluid View). The sigma data recorded covered the entire reservoir interval allowing comparison against previously acquired time lapse data while the CO logging was targeted at a much shorter reservoir section of high importance. The new Fluid View technique is seen to be complementary to the sigma analysis with data being acquired contemporaneously over the entire reservoir and has significantly less salinity dependence than traditional sigma but has a data acquisition speed which is half that of sigma and 20 times faster than CO. Analysis of the combined data sets provided valuable insights into saturation, vertical profile of the water sweep, current fluid movements and has led to a better understanding of residual oil saturation and distribution behind the flood front. Introduction Mungo is an oil field with a small free gas cap located within the Eastern Trough in the Central North Sea, 143 miles east of Aberdeen in water depths of around 89 meters (Figure 1). The field comprises a diapir flank structure with hydrocarbon reservoirs mainly in Palaeocene turbidite sandstones sealed beneath Eocene mudstones and with an up-dip termination against Zechstein salt. The Palaeocene sandstone lies above a hydrocarbon-bearing chalk reservoir (Holt et al 2000). The major hydrocarbon phase consists of oil with a density of about 37 degrees API derived from Upper Jurassic mudstones buried deep within the nearby axis of the Eastern Trough. As part of the field depletion plan, a new phase of drilling was scheduled for 2008 with well location W1 as a potential target. One of the key uncertainties in assessing the W1 target was the influence of injection water from well W164 to the W160 well location (Figure 2). A number of alternative scenarios were considered and reservoir simulation models built which achieved valid history matches with flow paths either deep - around the base of the structure, or shallow – across the thinner upper sands. These two realisations have markedly different impact on the W1 target and the type of well architecture needed to access it. Saturation data from well W164 is important in understanding which of the two scenarios is more likely. (Figure 3) (Rattey et al 1997) Logging History Well W164 was completed in 1999 as an injector in Maureen M2 which is below the target Upper Palaeocene sands (Figure 4). The first cased hole saturation surveillance was in 2005 when CO and PNC logs were gathered.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.45)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock (0.45)
- Europe > United Kingdom > North Sea > Central North Sea > Forties Formation (0.99)
- Europe > United Kingdom > North Sea > Central North Sea > Central Graben > Block 23/16a > Eastern Trough Area Project > Mungo Field (0.99)
- Europe > United Kingdom > North Sea > Central North Sea > Central Graben > Block 22/20 > Eastern Trough Area Project > Mungo Field (0.99)
- (3 more...)