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Collaborating Authors
SPE Rocky Mountain Regional Meeting
This paper was prepared for presentation at the 1999 SPE Rocky Mountain Regional Meeting held in Gillette, Wyoming, 15–18 May 1999.
- North America > United States > Colorado (0.89)
- North America > United States > New Mexico (0.70)
- North America > United States > Wyoming > Campbell County > Gillette (0.24)
- Geology > Rock Type > Sedimentary Rock > Organic-Rich Rock > Coal (0.48)
- Geology > Geological Subdiscipline (0.36)
- North America > United States > New Mexico > San Juan Basin > Fruitland Formation (0.99)
- North America > United States > Colorado > San Juan Basin (0.99)
- North America > United States > Arizona > San Juan Basin (0.99)
This paper was prepared for presentation at the SPE Rocky Mountain Regional Meeting held in Gillette, Wyoming, 15–18 May 1999.
- North America > United States > Wyoming > Rawhide Field (0.99)
- North America > United States > Wyoming > Powder River Basin (0.99)
- North America > United States > Montana > Powder River Basin (0.99)
- (11 more...)
The in A thick composite coal seam at Dartbrook Mine has been situ interference test and the laboratory tests sampled the coal characterised by stress and permeability testing over several permeability at a range of length scales comprising three years. Most recently, a four well injection interference test was orders of magnitude. Access to mine workings and to conducted to measure the overall seam permeability horizontal drill core provided a broader view of coal structure anisotropy, and core samples from various sites near the than is usually available for CBM reservoir evaluation.
- Geology > Rock Type > Sedimentary Rock > Organic-Rich Rock > Coal (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
This paper was prepared for presentation at the 1999 SPE Rocky Mountain Regional Meeting held in Gillette, Wyoming, 15–18 May 1999.
- North America > United States > Wyoming > Powder River Basin (0.99)
- North America > United States > New Mexico > San Juan Basin (0.99)
- North America > United States > Montana > Powder River Basin (0.99)
- (19 more...)
This paper was prepared for presentation at the 1999 SPE Rocky Mountain Regional Meeting held in Gillette, Wyoming, 15–18 May 1999.
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.51)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.42)
- North America > United States > Wyoming > Mountain Field (0.99)
- North America > United States > Wyoming > Bighorn Basin (0.99)
- North America > United States > Wyoming > Big Horn Basin > NPR-3 > Tensleep Formation (0.99)
- (3 more...)
Abstract The technical and economic benefits of integrating different information sources is well founded. It is important that drilling, well completion, and production personnel be included in such task forces if optimal information gathering is to be accomplished. The pulling together of people and data from several information disciplines, and utilizing individual strengths, while learning to accept specific weaknesses as such, leads to a cooperative team effort in resolving difficult reservoir characterizations. Introduction As our proven oil and gas resources diminish. and new sources become more difficult to find and produce, technology in virtually every inflation discipline has demonstrated dramatic advances. Despite the technical improvements, there are many cases where individual disciplines continue to achieve only marginal success. Too much impetus is directed toward self-promotion of a particular information discipline, and too often competitive disciplines are criticized for their particular weaknesses. Combining the attributes and strengths of different disciplines, and recognizing where each have inherent weaknesses leads to much more accurate analysis of subsurface real estate. When petroleum reservoirs are found with fewer exploration wells. and when accurate analysis of all the available information is utilized to appraise and describe newly discovered reservoirs, oil company economics is drastically improved. Case histories show:the positive aspects of using both seismic and borehole data to establish the size and location of a potential reservoir. the use of well logs, seismic, and borehole seismic methods to define the size. shape. and internal characteristics of a newly discovered reservoir, and the capability of planning and implementing the most economical and technical extraction of discovered reserves. Total integration of both surface and borehole data and personnel is cost-effective and leads to more effective reservoir evaluation. Effectiveness of information gathering is enhanced when drilling, well completion, and production personnel are made aware of the difficulties some procedures cause for the different information-gathering disciplines. Reconciling procedures before a problem arises is beneficial for all. Team Structures The traditional approach to reservoir description used processed seismic data, interpreted by geologists, to select prospective drill sites. Predrill economics would be discussed to determine the risks. Ultimately, the drilling department would be assigned its task, and their primary consideration was to drill a hole of a certain size to a prescribed depth, with little consideration of how their methods would affect the gathering of vital information from potential reservoirs. Mud logs, well-site cuttings descriptions, core data, drill-stem tests, well logs, and wireline tests would be used to monitor or measure selective intervals. Most core analysis and log and test analysis would be attended to after the well was drilled, and too often the information necessary to make timely, economic decisions about well completion or abandonment was utilized in a disorganized discussion of what each information source provided, until finally the responsible authority made his decision. Today, in a disciplined, interactive, integration effort, individuals from geophysics, geology, petrophysics, drilling, well completion, computer science, production, and reservoir engineering disciplines should be included (Fig. 1). Contributions must be more than a collection of results from different groups. P. 305^
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type (0.70)
- Geology > Mineral > Silicate > Phyllosilicate (0.48)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Surface Seismic Acquisition (0.47)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
Abstract The term 'cut-off' refers to a joint effort by geoscientists and engineers to define a value which discriminates non-reservoir rock (shale) from reservoir rock (sand), and has been used in the petroleum industry for several decades. In many instances a single cut-off is not good enough to define sand requiring the use of additional variable cut-offs. The determination of cut-off(s) is highly dependent on a geoscientist's or engineer's experience. There exists no well-defined method that is built on a sound scientific basis. A systematic method is presented in this paper. The new method separates reservoir rock from non-reservoir rock based on the statistical properties and probability distribution functions derived for the various properties of a reservoir interval. It is common that the histogram of a variable (i.e. porosity) has a mixed interval between reservoir and non-reservoir rocks. No matter how a cut-off is defined on the histogram, there will always be a number of values that are incorrectly classified. As a result, we have seen errors in hydrocarbon volume calculation which have been in the range of 5 to 30 %. Unlike the typical application of cut-offs, the proposed method identifies a boundary (no or little mixing) between the two rock populations and then separates them based on this boundary. Correct boundary identification, by analyzing all available variables collected from well logs, is the key to success. When there is no clear boundary, integration of engineering data (i.e. relative permeability and capillary pressure) with the well logs helps to identify possible boundaries or cut-offs. A field study demonstrates the principle of the new method and the improved results includes more than 80 wells. The method is easy to understand and to apply in practice. Introduction Frequently, geoscientists and engineers in the petroleum industry refer to non-reservoir rock and reservoir rock as shale and sand, respectively, despite the lack of pure shale and pure sand compositions for sandstone reservoirs. We will adopt this terminology in this paper, further noting that this method applies for carbonates and siliciclastics alike. Separating shale from sand is critical in the process of building a geological and reservoir simulation model. Sand and shale have very different characteristics of lithology, porosity, permeability, and water saturation. In contrast with shale, sand typically possesses much larger values of porosity and permeability, and contains much smaller values of water saturation. Sand stores oil and gas, and provides flow channels for reservoir fluids, while shale acts as flow barriers. These flow barriers greatly impact sweep efficiencies of waterflood and EOR processes. Though defining petrophysical cut-off values is routine work for geoscientists and reservoir engineers, methods based on well log petrophysical data for separating shale from sand are ill-defined. Geoscientists and reservoir engineers still rely heavily on empirical cut-off values (e.g. porosity, permeability, and water saturation etc.) to predict sand/shale distributions and net pay. Different companies and different interpreters have their own criteria for determining cut-off values. Through application of advanced reservoir characterization techniques, such as geostatistics, capabilities exist to model sand and shale in three dimensions and in great detail. The industry requires a well-defined and consistent method to correctly distinguish shale from sand and to properly model sand/shale distributions. The following sections describe and discuss a statistically-based technique via both theory and an actual field application. P. 285^
Abstract The increasing need to improve production from developed oil and gas fields requires more detailed and more accurate reservoir description. Our answer is a multidisciplinary approach integrating geophysics, geology, geostatistics, and engineering. For a field with seismic data, well logs, and production data, an integrated reservoir model can be more accurate than the conventional approach of passing the model from geophysicists to geologists then to engineers. In the development stage of a field, a geological model would have been built. The model would have been tested against production data. Now If we are going to build a more detailed model, the existing reservoir model is a valuable input. When dealing with seismic data, time permitting, known information of structure and stratigraphy may be used. When converting time data to depth, well data may be used together with seismic information to build a 3D velocity model. Geostatistics may be used with velocity logs and seismic data to build a more accurate and detailed velocity model. Without a reasonable velocity model for time depth conversion, it is difficult to integrate well data and seismic data. A lithology model or porosity model may be built with well data, structure information, and seismic attribute as soft data. Seismic attributes are calibrated to well data. Geostatistics provides a way to properly weight between well data and seismic data. The reservoir model may be compared or calibrated with production data. Then we may go back and modify the velocity model or horizon picks, or we may modify the conversion from seismic attribute to soft data used in the geostatistical gridding. By iteration, our reservoir model will become more accurate because it honors geophysical, geological, and engineering data. Common data links and tools like geostatistics make the integration easier. Field applications show that an integrated iterative approach to detailed reservoir modeling can provide a better model and help optimize production. Introduction Reservoir description is an essential tool for producing more hydrocarbon at lower cost. Depending on the purpose of the reservoir description, the complexity and time required can be very different. For the best results, integration is the key. Integration includes integration of various disciplines, integration of various data types and various models, and a common reservoir description that can be reconciled from various aspects. Let's consider a typical reservoir description process from seismic to production data. In a non-integrated approach as shown in Fig. 1: seismic data is processed without the benefit of a reservoir description model, horizons are picked and used as is, geological model is built without understanding how the seismic data was interpreted, then a reservoir simulation model is built with modification to the geological model in order to match production history without going back to the geologist or geophysicist. There are at least four reservoir models here: one velocity model for seismic processing, one velocity model for time-depth conversion, one geological model before reservoir simulation, and one geological model in reservoir simulation. It is not uncommon that these models become inconsistent, as illustrated in Fig. 2. When each step of the modeling process is isolated, the tendency is to modify the reservoir model in such a way that other workers may not agree from their understanding of the data. In a non-integrated process, each reservoir model, such as seismic velocity models, geological model, and engineered reservoir model actually used in reservoir flow simulation, is modified to honor one group of data but may contradict other groups of data. The result is that each reservoir model is less realistic than the available data and the predictions are less accurate than possible with an integrated approach. When we need to modify the reservoir model to honor our data, which part should we change? P. 295^
Abstract Formation permeability is one of the most important parameters for reservoir evaluation and optimization. Despite its importance, accurate estimation of continuous permeability is difficult due to its high variability and its dependence on the scale of measurement. To date, there is no generally accepted logging method for evaluating the continuous permeability of a formation. However, recent advances in the NMR logging (CMR* Combinable Magnetic Resonance tool) hold the greatest promise for a generally accepted continuous measurement of formation permeability. We present comparison of permeability estimates from the CMR tool with the permeability estimates from other sources (i.e., cores, well tests, formation testers, and minipermeameter). Because permeability can vary by several orders of magnitude over short vertical distances and because different sources measure permeability at different scales, it is essential that permeability be properly averaged (arithmetic, harmonic or geometric depending on the type of heterogeneities) before such comparisons are made. With the help of field examples, we show the utility of permeability esimates from the CMR tool for reservoir engineering applications. Due to the empirical nature of the permeability evaluation from T2 distributions, the absolute value of permeability must be calibrated with other sources of permeability information. However, the variation of permeability in a zone is estimated quite accurately. The continuous record of the variation of permeability in a zone is useful for establishing a completion/stimulation strategy and for establishing optimum reservoir management through reservoir simulation. Introduction Of all the formation parameters that are needed in the evaluation of hydrocarbon resources, permeability, which is a measure of how easily a fluid of certain viscosity flows through the rock under a pressure gradient, is the most important and, at the same time, the most difficult to evaluate. It is vital for establishing producibility, which determines whether a well should be completed and brought on line. The knowledge of the level and distribution of permeability is also essential in reservoir management for choosing the optimal drainage points and production rate, designing perforations, and selecting a stimulation strategy. Permeability of sedimentary rocks has a wide range, from near zero in some shales to 10,000 mD in coarse-grained sandstones. It can also vary greatly within a few inches in the same formation. Permeability of a formation is determined by the complex interaction of a number of variables. During deposition the grain size and the degree of homogeneity of the original particles are primary factors in determining permeability. How densely these particles are arranged, both initially and later during burial, can significantly alter pore diameters and subsequent permeability of the reservoir. Permeability generally increases with porosity, grain size and certain bedding patterns, but these relationships are far from consistent. Some apparently highly permeable rocks are rich in clay that migrates with production and plug pores, diminishing permeability. On the other hand, some relatively impermeable rocks may produce copiously through a network of fractures. In general, permeability increases with the size and interconnectedness of a rock's pores, but unforeseen details are frequently influential and extremely difficult to account for. This makes the estimation of permeability a difficult task under most circumstances.
- North America > Canada (0.78)
- North America > United States > Wyoming > Big Horn County (0.15)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.55)
- Geology > Structural Geology > Tectonics > Compressional Tectonics > Fold and Thrust Belt (0.41)
- North America > United States > Wyoming > Wind River Basin > NPR-3 > Muddy Formation (0.99)
- North America > United States > Wyoming > Grass Creek Field (0.99)
- North America > United States > Wyoming > Bighorn Basin > Sage Creek Field (0.99)
- (7 more...)
Petrophysical Analysis of the Frontier Formation (Cretaceous), Whiskey Buttes Field, Lincoln County, Wyoming
Cluff, Suzanne G. (The Discovery Group, Inc.) | Cluff, Robert M. (The Discovery Group, Inc.) | O'Connor, Leslie S. (Schlumberger GeoQuest Reservoir Technologies) | Hansen, John T. (Gas Research Institute)
Abstract A petrophysical study of the Frontier Formation in the Whiskey Buttes Field was performed as part of the GRI project "Emerging Resources in the Greater Green River Basin". Tight gas sands in general, and the Frontier Formation in particular, are difficult to evaluate and predicting long term well behavior is a problem. This study provided a detailed petrophysical characterization of the Frontier Formation which allowed us to compare reservoir characteristics to well performance. Thirty-five representative wells were selected for analysis. The wells were chosen to provide full geographic coverage of the field, to cover the entire range of porosity and permeability and to span the range of production from dry holes to 7 BCF wells. Seven of the wells had routine core analysis available and these were used to calibrate the petrophysical model. A complete shaly sand analysis was performed. Two different pay cases were calculated, generating average effective porosity, permeability, and water saturations for each stratigraphic zone. Also the net feet of hydrocarbon pay volume [h* *(1-Sw)] was calculated for each zone in each well. These pay parameters were then compared with estimated ultimate recovery from decline curves to evaluate which factors could be used to predict well performance. While several trends could be discerned, no one or two factors alone can be used to predict well performance. Log calculated permeabilities were the best predictor, although not a highly reliable one. The fluvial section of the Frontier appears to contribute more to production than the marine zones. Most of the wells drain less than 160 acres. The major problem is that many of the factors affecting production cannot be quantified, including the effectiveness of the hydraulic fracture treatments, presence of natural fractures, and the effective drainage area for each well. Setting The Whiskey Buttes Field is located on the Moxa Arch, in Sweetwater and Lincoln Counties, Wyoming (Figure 1). The field was discovered by Amoco in 1975 through a combination of seismic and subsurface geology. The discovery well, the Whiskey Butte Unit #1 (NW/4 SW/4 S24-T22N-R111W), was completed in the Second Frontier Formation with an initial potential of 1995 MCFGPD and 94 BCPD. As of 1992, the field wide ultimate recovery for the Frontier Formation alone was estimated to be 247.3 BCFG. Although some of the production is from the Dakota Formation (Lower Cretaceous), the majority of the completions and production are from the Second Frontier Formation (Upper Cretaceous). The Second Frontier has been further subdivided into several locally mappable benches; all but the top bench are marine deposits composed of shoreface, coastal plain, and shoreline facies. The first (top) bench of the Second Frontier is a continental deposit consisting of fluvial valley fill and alluvial plain facies. Nearly all of the wells at Whiskey Buttes were perforated in both the first and second benches, including the fluvial sandstones and the uppermost part of the marine section, but several wells were also completed in the deeper third and fourth benches. We used the stratigraphic nomenclature and correlations proposed by Forster and others as the stratigraphic framework for this study. P. 271^
- North America > United States > North Dakota > McKenzie County (0.82)
- North America > United States > Wyoming > Lincoln County (0.60)
- Geology > Geological Subdiscipline > Stratigraphy (0.95)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.91)
- Geology > Sedimentary Geology > Depositional Environment > Continental Environment > Fluvial Environment (0.66)
- North America > United States > Wyoming > Sand Wash Basin (0.99)
- North America > United States > Utah > Sand Wash Basin (0.99)
- North America > United States > Utah > Green River Basin (0.99)
- (4 more...)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Core analysis (1.00)