Layer | Fill | Outline |
---|
Map layers
Theme | Visible | Selectable | Appearance | Zoom Range (now: 0) |
---|
Fill | Stroke |
---|---|
Collaborating Authors
Results
In making the petrophysical calculations of lithology, net pay, porosity, water saturation, and permeability at the reservoir level, the development of a complete petrophysical database is the critical first step. This section describes the requirements for creating such a database before making any of these calculations. The topic is divided into four parts: inventory of existing petrophysical data; evaluation of the quality of existing data; conditioning the data for reservoir parameter calculations; and acquisition of additional petrophysical data, where needed. The overall goal of developing the petrophysical database is to use as much valid data as possible to develop the best standard from which to make the calculations of the petrophysical parameters. Inventory of Existing Petrophysical Data To start the petrophysical calculations, the data that have been gathered previously from various wellbores throughout the reservoir must be identified, organized, and put into electronic form for future calculations.
- North America > United States > Texas (1.00)
- Asia > Malaysia (0.93)
- North America > United States > Alaska > North Slope Borough > Prudhoe Bay (0.68)
- (2 more...)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (1.00)
- Geology > Geological Subdiscipline (1.00)
- Geology > Mineral > Silicate > Phyllosilicate (0.71)
- (2 more...)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Surface Seismic Acquisition (0.46)
- South America > Ecuador > Pastaza > Oriente Basin > Block 10 > Villano Field (0.99)
- North America > United States > Wyoming > Greater Green River Basin > Carter Creek Field (0.99)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- (33 more...)
Abstract Hydraulic fracturing in the secondary recovery units in the Permian Basin of west Texas is often complicated by a number of factors. Generally, there exists a lack of historical data or information required to effectively design the desired treatment. Therefore, treatments in these fields have often been "cook-booked" and given less engineering attention due to their smaller size and scope. Many times the process is further complicated by the iterative nature required in effective treatment modeling (i.e., historical review, candidate selection, pre-job design, pre-job diagnostics, on-site or post-job modeling, and post-job diagnostics). In this paper, we will outline the steps required to improve these processes without expending excessive resources. Then, we will discuss steps where streamlining the process is warranted without compromising the end result, Finally, we will document and present several cases illustrating effective use of these technologies to obtain more accurate stress profiles and improved fracture treatments in secondary, in-fill development projects. P. 281
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Passive Seismic Surveying (0.46)
- 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)
- (29 more...)
One Core, Few Modern Logs, and Limited Production Data: Is Reliable Reservoir Characterization Possible?
Chawathe, A. (New Mexico Petroleum Recovery Research Center) | Ouenes, A. (New Mexico Petroleum Recovery Research Center) | Ali, M. (New Mexico Petroleum Recovery Research Center) | Weiss, W. (New Mexico Petroleum Recovery Research Center)
Abstract Limited and unreliable data has been the bane of reservoir characterization. This is especially true for fields which were developed before the 70's, when field development was more art than science. Producers struggling with re-engineering such fields have to evaluate every shred of available information to optimize modern production operations. This is a common problem faced by most operators today. In this Department of Energy (DOE) sponsored project, we characterize one such field, the Sulimar Queen, using recent technology coupled with conventional reservoir engineering. The Sulimar Queen - located in southeastern New Mexico - is a part of the Permian shelf. The producing formation is the upper Queen (Shattuck member) at 2000 ft. The original database comprised some pre-70's gamma ray and neutron logs, and production data from 35 wells. One well (well 1-16) contained modern gamma ray, resistivity, and porosity logs. In addition, a single core was available in this well. A successful characterization of the Queen was done based on this meager data set. The methodology entailed a multidisciplinary team to characterize the Sulimar Queen. On recommendations from the team geologists, additional data was collected from the Sulimar Queen outcrops and other adjacent fields. Transient tests were conducted and advanced core analyses were performed on the single available core. In this paper, we discuss the integration of all the assimilated data, which spans from the micro-scale (thin sections) to megascale (outcrops), to characterize the Sulimar Queen. Applications of new artificial intelligence tools, such as fuzzy-logic, to correlate thin-section data with permeability were developed. The use of geostatistics and simulated annealing to build simulation-scale property distributions are discussed. In addition, we present simulation scenarios based on honoring the dynamic production data with automatic history-matching, and 3D visualizations. The final version of the conditioned Sulimar Queen model includes a previously undetected gas cap. In conclusion, a geologic model of the Sulimar Queen honoring the observed data is presented. This geologic model incorporates all major depositional events and their effects on reservoir properties. Introduction An understanding of the reservoir structure and developing characteristic measures for heterogeneity classification are essential to maximize oil production from producing reservoirs. The physical phenomena associated with oil recovery have been relatively well understood for some time. Nevertheless, there have been disappointing gaps between model predictions based on laboratory and field tests, and the actual production of oil. The scarcity of detailed reservoir data has contributed to such failures. A unique opportunity for a detailed and integrated study existed at the Sulimar Queen, which was available as a research field. One goal of the Sulimar Queen study was to use the integrated data to explain its waterflood success and to share the insight gained in this study with operators re-engineering reservoirs similar to the Sulimar Queen. The reservoir characterization methodology, resulting from history-matching the primary production to predict secondary recovery performance, can be readily utilized by oil and gas producers. The challenge of the Sulimar Queen project was to develop a reasonable reservoir model using limited and old data - a common problem with reservoirs in the Permian Basin. Typically, old well logs (gamma ray-neutron perforating logs) and production history are the only data available for this maturing area. The methodology developed to deal with such data will be briefly explained, addressing each data type in an ascending order of scale. Sulimar Queen: A Typical Old Field. The Sulimar Queen unit is typical of many old fields lacking high-quality reservoir data. In most cases, this situation prevents an operator from investigating the future potential of such fields. For example, an infill drilling program would require at least the spatial mapping of key reservoir properties. P. 109^
- North America > United States > Texas (1.00)
- North America > United States > New Mexico > Chaves County (0.28)
- Geology > Geological Subdiscipline (0.95)
- Geology > Mineral (0.72)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.71)
- Geology > Sedimentary Geology > Depositional Environment > Transitional Environment (0.47)
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > North America Government > United States Government (0.67)
- 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)
- (25 more...)
Distinguished Author Series Summary This paper gives methods to characterize tight gas reservoirs in sufficient detail to allow an engineer to make accurate long-range production forecasts. These forecasts are the basis for sound engineering and business decisions. Because of the complexity and variability of tight gas reservoirs, we can present only general procedures for developing reservoir descriptions. Accordingly, we illustrate a reservoir characterization method with three examples of successful tight gas reservoir studies. The procedures in these examples can be modified as needed for other specific formations or areas. Introduction Production rates from many tight reservoirs are marginal, but these reservoirs account for a large percentage of the long-term gas supply. Because of the marginal economics, efficiency is the key to drilling and producing these tight reservoirs. To optimize production, we must have a good understanding of the reservoir, but often the economics cannot support collecting the data necessary to describe the reservoir properly. A reservoir engineering study for tight reservoirs requires us to balance data collection costs with the level of detail necessary to describe the reservoir accurately. One must determine what level of reservoir characterization is needed to optimize production from tight reservoirs efficiently. Unfortunately, because of the diverse nature of tight reservoirs, there is no single answer. The question must be answered on a case-by-case basis. Reservoir studies of tight reservoirs are performed to meet many different objectives. Because tight wells require hydraulic fracturing, fracture treatment optimization studies are quite common. A reservoir study is sometimes performed in conjunction with a detailed geologic study to help identify key well characteristics or field trends to be used as exploration tools and to predict reserves. A reservoir study can identify infill-well potential and the potential for increased productivity and reserves as the result of the installation of compression or liquid lift equipment. Finally, reservoir studies can resolve conflicting data or determine why some wells are notproducing as expected. Unfortunately, analysis of tight reservoirs is one of the most difficult problems facing a reservoir engineer. Many tight formations are extremely complex, producing from multiple layers with permeabilities that often are enhanced by natural fracturing. Unfortunately, low productivity and marginal economics often prevent expenditures of money and time to collect the data needed for a detailed reservoir study. Because the permeability of these formations is low, many standard formation evaluation techniques do not provide adequate results. Standard log-based correlations for permeability or other productivity indicators often fail in tight reservoirs, so correlations must be developed on an area-specific basis. Many tight shale reservoirs have productive gross intervals exceeding 300 ft, making it difficult to determine where the gas is produced, thus complicating completion decisions. Even in tight gas sands made up of interbedded sands and shales, layering can have a pronounced effect on well production. Natural fractures often occur in these tight formations, making wells that appear similar on logs perform quite differently. When we do not describe the reservoir in sufficient detail, the production forecasts we generate are frequently wrong. Unless we can predict postfracture well performance accurately, we cannot optimize the fracturing process. Sound business decisions regarding compressor installation, infill drilling, or remediation treatments are not possible. Unfortunately, for layered reservoirs, oversimplified reservoir descriptions frequently result in an overestimated well productivity. Fig. 1 shows predicted 20-year performance for a Devonian shale well for three different reservoir descriptions: "lumped" one-layer, 3-layer, and 10-layer reservoirs. All three predictions are based on the same gas in place and the same total permeability-thickness product, kh. Note that the lumped one-layer model over predicts the gas recovery by a factor of two. The four-layer model prediction is closer to actual but is still high by about 17%.Any business decisions based on the single-layer prediction would be seriously in error. Background Development of tight gas reservoirs has been increasing substantially over the last decade. Because of this trend, the Gas Research Inst. (GRI) and the U.S. DOE have been funding detailed research in tight gas sands and shales throughout the U.S. This research has led to significant advances in hydraulic fracturing and a better understanding of the complexity of the tight reservoirs. The importance of describing a layered reservoir has been discussed in the literature for several decades. Much of this discussion has centered on pressure-transient analysis of layered reservoirs (with and without crossflow)and descriptions of the nonideal buildup test pressure responses often observed in the field. Lefkovits et al. presented analytical solutions for flow in layered reservoirs and identified several characteristic features of reservoirs with discrete, noncommunicating layers. Other investigators presented numerous solutions describing pressure and flow rate that include the effects of interlayer crossflow, stimulation, or unsteady- (transient) or pseudo-steady-state (boundary-dominated) flow. Comprehensive analytical reservoir models have been developed specifically to model the pressure or flowrate response from layered reservoirs. Although much theory has been presented in the literature, case studies documenting layered reservoir analyses are not as common. Much has been presented on fracture treatment optimization in tight reservoirs, but generally the impact of layering is not discussed. The majority of layered reservoir analyses involve history matching data by use of reservoir simulators, although the recent availability of comprehensive analytical models should make layered analyses easier and more cost-effective. Even with sophisticated computer programs for analysis of layered reservoirs, this analysis is still not straightforward. Cost-effective data collection methods for describing layered reservoirs are not developed easily because of the diverse nature of tight reservoirs and widely varying production volumes. P. 956^
- North America > United States > Texas (0.93)
- North America > United States > Kentucky (0.68)
- Asia > Middle East > Israel > Mediterranean Sea (0.34)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Tight gas (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
- (4 more...)
Summary Data acquisition design and implementation challenges for mature reservoirs which are targets for Improved Oil Recovery (IOR) applications are discussed in this paper. Examples are provided for Shallow Shelf Carbonate (SSC) reservoirs in the Permian Basin of West Texas. What Are Mature reservoirs? Mature reservoirs are defined as properties with additional recovery potential by implementation of advanced reservoir characterization tools and techniques, reservoir management and/or changes in recovery mechanisms. Attributes of mature reservoirs are depicted in Figure 1, which shows the importance of reservoir characterization as a function of field development stage. Reservoir characterization and an understanding of heterogeneity become more important for maturing reservoirs as these factors have a profound impact on future reservoir development and management strategies. Mature reservoirs are typically characterized by some type of secondary drive mechanism. A change to a tertiary mode or implementation of other lOR methods may be necessary to extend the economic limit and productive life of the field. A team approach is also important to achieve data acquisition objectives in mature reservoirs. However, the data acquisition situation may be very different from that "new" reservoirs. The desire and need for IOR may be critical as the economic limit may be rapidly approaching and data required for IOR may not be available. Smaller reservoir size and lower remaining reserves may present economic constraints towards the acquisition of essential data for the implementation of many IOR methods. The lack of production, fluid properties and other data in the earlier stages of field development may present uncertainties in history matching with numerical simulation methods. This results in unreliable reservoir performance forecasts for IOR. Often, the implementation of data acquisition programs in mature reservoirs present opportunities to enhance near-term reservoir performance through effective reservoIr management. Data acquisition strategies for properties which are being considered for abandonment are not addressed in this paper. Redevelopment of these properties is often required to exploit behind pipe potential and undeveloped zones or horizons. INTRODUCTION - DATA ACQUISITION METHODOLOGY The data acquisition process for mature reservoirs can be segmented into two major areas:
- Geology > Sedimentary Geology > Depositional Environment (1.00)
- Geology > Geological Subdiscipline > Stratigraphy (0.69)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.46)
- Geophysics > Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- 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 This paper presents the application of a new analytical simulator specifically developed for multilayer reservoirs. In this paper, we demonstrate how this analytical simulator can be used, the same way as a conventional numerical simulator, to history-match field pressure data, production data, and forecast reservoir performance. Two field cases are presented in which analytical simulation results and numerical simulation results are compared. Introduction It has been shown that an equivalent single-layer reservoir model is not as accurate when describing a well completed in a layered reservoir. Usually, the single-layer model will be optimistic. Developing a reservoir description for a multilayer reservoir system often requires a geological analysis, well logging data, core data, in-suite stress information, the characterization of natural fractures (if applicable), and well test data. A reservoir description developed with these data can provide the basis for a better understanding of a multilayer system. Once a multilayer reservoir description has been developed, an efficient and accurate reservoir analysis tool is needed for studying this kind of reservoir. To study a multilayer reservoir, it is often necessary to history match production and well test data and to make production forecasts. we have found that conventional numerical simulators can be difficult to set-up and time consuming to run, especially for reservoir containing more than one layer. In this paper, we present the application of a new analytical simulator developed specifically for a multilayer system. Both oil and gas reservoir systems can be modeled. Our new analytical reservoir simulator is capable of modeling the performance of commingled reservoirs with unequal initial pressures in different layers and allows each layer to be homogeneous or dual porosity, hydraulically fractured or have radial flow (with skin), and be finite or infinite in extent. In addition, this simulator can model constant rate production and constant bottom-hole pressure production. Pressure buildup tests following both constant bottom-hole pressure production and constant rate production can also be modeled. The advantage of using this analytical simulator is that it is much easier to use and faster than conventional finite-difference reservoir simulators. Description of the Analytical Simulator Reference 1 presents the details of the development of the analytical solutions to the reservoir performance of a commingled system by using Laplace transforms to solve the partial differential equations. The analytical solution for each layer can be combined together with inner boundary conditions at the common wellbore to simulate multilayer reservoirs. Pseudo variables were used for gas systems. Since each layer may have different gas properties, due to either different initial pressure or differential depletion, pseudo times are defined based on each layer's gas properties and the averaged gas properties for the total system. A multiple time scale concept was used when taking Laplace transforms and inverting Laplace transforms numerically. This analytical simulator can provide wellbore pressures. layer sand face rates, layer average pressures, and layer cumulative production. Both production and shut-in periods can be modeled. The production can be either constant rate of constant pressure production. Field Case Application In this section, we discuss how this analytical simulator can be used in the same way as numerical simulators, to analyze pressure transient data, production data, and make long-term performance forecasts for two wells located in the United States. The two cases discussed in this section were originally presented in SPE papers. In these papers. the authors state that they used a numerical simulator to analyze pressure transient and production data and to developed a multilayer reservoir description, with which to make long-term performance forecasts. Reservoir models for both wells were developed based on core data, log data, and well test data. To demonstrate our new analytical simulator as an efficient and accurate reservoir study tool, we used it to rerun the simulation using the same reservoir description, or a similar reservoir description with little modification, as the ones in the literature and compare our results with the field data and the results of numerical simulations. P. 413^
- 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 > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Pressure transient analysis (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Drillstem/well testing (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring (1.00)
ABSTRACT This paper describes the planning, design, execution and results of massive fracture acidizing treatments in the Tommeliten Field, offshore Norway. The Tommeliten is a marginal field consisting of subsea completions. Difficulties in effectively stimulating the Tommeliten wells arise from numerous factors including the relatively soft and homogeneous nature of the chalk reservoir, large intervals requiring staged treatments, high treating pressures, high formation temperatures, and the logistics of performing the treatments via a semi-submersible drilling rig and stimulation vessel. Based on experience in other chalk formations, a general treatment program was formulated. Treatment design utilized multi-stages of viscous pad followed by acid and over flush. Fracture geometry was simulated by means of computer design programs. Extensive laboratory testing was then carried out to provide data for improving the program. As each well was treated the program was modified based upon information from pre-fracturing injection tests and the main treatment profiles. One and one-half years production data were available at the writing of this paper and form the basis for the productivity evaluation. INTRODUCTION The Tommeliten Field is situated in block 1/9 in the western part of the Ekofisk Area of the North Sea (Fig. 1). This field is operated by Statoil with Norske Fina and Norsk AGIP as partners. The field consists of two distinct and separate structures, Alpha and Gamma. Phase I of the Tommeliten project consisted of six wells in the Gamma structure. This project, a subsea development in water depth of 250 ft, is based on the use of existing infrastructure of the Ekofisk complexes. The six Gamma wells have been drilled through a single subsea template and are tied to the Edda platform, 7.5 miles to the northeast, via a subsea manifold. Drilling of the Tommeliten wells was described by Sunde. The subsea template has been described by Solheim. Completion and stimulation of these wells took place during the months of May-August 1988 and contractual gas deliveries commenced in early October 1988. Reserves in the Tommeliten Field are found in the Ekofisk and Tor formations, chalk sediments of the Tertiary and Upper Cretaceous Period (Fig. 2). The reservoir is a massive chalky limestone with high porosity and low matrix permeability, similar to other reservoirs in the Ekofisk Area. Combined thickness of the Ekofisk and Tor intervals ranges from 300 to 700 ft. Pressure and temperature at a depth of 10,168 ft mean sea level (MSL) are approximately 7,050 psi and 265°F. The gas/oil ratio was expected to be about 1,470 Sm /m. Statoil discovered the Tommeliten field in 1976, but its relatively small size and a lack of realistic commercial outlet for the gas delayed development. One significant benefit of the long lead time between discovery and production is the extensive experience which has been gained regarding stimulation techniques for North Sea Chalks and specifically for Ekofisk Area wells. REVIEW OF STIMULATION TECHNIQUES Chalk reservoirs are an exploration objective in many parts of the world. Chalks typically have high porosity, however, due to their low permeability they are only marginally profitable unless they can be effectively stimulated.
- Europe > Norway > North Sea > Central North Sea (1.00)
- North America > United States > Texas > Harris County > Houston (0.28)
- Geology > Mineral (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock > Limestone (0.74)
- 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)
- (39 more...)