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ABSTRACT Compressive sensing is used to improve the efficiency of seismic data acquisition and survey design. Nevertheless, most methods are ad hoc, and their only aim is to fill in the gaps in the data. Algorithms might be able to predict missing receivers’ values, however, it is also desirable to be able to associate each prediction with a degree of uncertainty. We used beta process factor analysis (BPFA) and its variance. With this, we achieved high correlation between uncertainty and respective reconstruction error. Comparisons with other algorithms in the literature and results on synthetic and field data illustrate the advantages of using BPFA for uncertainty quantification. This could be useful when modeling the degree of uncertainty for different source/receiver configurations to guide future seismic survey design.
Abstract This paper demonstrates how to utilize seismic data to create a variance cube with different values and convert it into a transmissibility multiplier pattern to assist the history match in dynamic simulation. Lateral variations of the seismic signal can be related to various reasons, such as faults respectively associated fault damage zones and sedimentological bodies like channel features. Dissimilarities within the seismic data can be emphasized by calculating a variance attribute. The different value ranges within the variance cube can in the chosen examples be associated to the fault plane itself and different intensities of fault related rock alterations or channel characteristics, e.g. erosional planes. These features can result in improved or reduced lateral transmissibility as poor or good rock properties coincide with poor or good transmissibility for fluid flow. The variance value patterns can be analyzed and categorized according to the discrete value ranges. Then the categories are converted to reference numbers, e.g. variance reference number 1 represents a good transmissibility (e.g. a channel body), 2 represents reasonable and 4 represents the poorest class (e.g. an intense faulted zone). Subsequently the variance reference number is converted to a transmissibility multiplier value. During the history match process, these multipliers are adjusted in order to improve the history match. The advantage of this approach is that it does not only change the transmissibility multiplier in one cell, but changes the transmissibility multiplier value of each cell that belongs to one type of rock quality (one variance reference number or one transmissibility group) in one calculation. This is especially useful where there is limited history data and the variance data indicates similar patterns throughout the variance cube. This method can speed up the history matching as it is a batch change of transmissibility multipliers, similar to the fault transmissibility multiplier method in ECLIPSE but with a wider application. In a case study a comparison was conducted with using the variance cube / transmissibility multiplier patterns and confirmed the method workable. In conclusion using the variance cube to create transmissibility multiplier patterns is an efficient way to assist and improve history matching, especially in fields with limited well control. This paper presents a new way of creating transmissibility multipliers based on seismic data (variance attribute) and assisting and improving the history match process
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 038 > Block 15/12 > Varg Field > Sleipner Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 038 > Block 15/12 > Varg Field > Skagerrak Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 038 > Block 15/12 > Varg Field > Hugin Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 038 > Block 15/12 > Varg Field > Heather Formation (0.99)
Abstract When the Underground Injection Control (UIC) Regulations were promulgated in 1980, by the U.S. EPA, existing Class II (oilfield) injection wells operating at the time that the regulations became effective were excluded from Area of Review (AOR) requirements. EPA has expressed its intent to revise the regulations to include the requirement for AORs for such wells. A Federal Advisory Committee (FAC) has recommended that AORs for existing wells, not previously subject to that requirement, be performed within five years of promulgation of amended UIC regulations. The FAC has, however, recognized that conditions can exist that make it unnecessary to perform well-by-well AORs and that can allow wells in a basin, producing trend, region or field or a portion of such areas to be exempted from an AOR through a variance program. A methodology for identifying areas that would be eligible for variance from AOR requirements has been developed. The methodology provides for evaluation of an area for variance based upon five criteria. These criteria could be used in any order, singly or in combination, to exclude some or all wells from the AOR process. Wells not excluded by variance would be subject to well-by-well AORs. Introduction The Underground Injection Control Regulations promulgated by the U.S. EPA in 1980, under the Safe Drinking Water Act of 1974, require Area-of-Review (AOR) studies be conducted as part of the permitting process for newly drilled or converted Class II (oilfield) injection wells. Existing Class II injection wells operating at the time regulations became effective were excluded from the AOR requirements. In January 1988, the EPA initiated a Mid-Course Evaluation (MCE) of the adequacy of its regulations for Class II injection wells and, in August 1989, published a report of its findings. As a result of the MCE, EPA's Office of Drinking Water identified areas of concern to be further studied. Among the areas of concern was the need to further evaluate AOR requirements. In April 1991, the agency proposed and did form a Federal Advisory Committee (FAC) whose charge was to identify regulatory gaps and make recommendations for program changes where appropriate. P. 499^
- North America > United States (1.00)
- Asia > Middle East > Israel > Mediterranean Sea (0.54)
- Law (1.00)
- Government > Regional Government > North America Government > United States Government (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- North America > United States > New Mexico > San Juan Basin (0.99)
- North America > United States > Colorado > San Juan Basin (0.99)
- North America > United States > Arizona > San Juan Basin (0.99)
Abstract Structural uncertainty is defined by creating stochastic error surfaces built on control points. Uncertainty is zero at the drilled locations and varies smoothly away from the wells. The other factor that enhances uncertainty is fault-zone. This study aimed at generating a composite model integrating these two determinants of structural uncertainty. The study is done on Mauddud surface in part of the Greater Burgan Field, Kuwait. The seismic guided surface was created incorporating tops of 13 drilled wells. Sequential Gaussian Simulation was used to generate stochastic error surfaces having normal distribution using these 13 zero value control points as input. Deviation of the actual Mauddud top from the given seismic surface was calculated to be to the tune of ±60'. The stochastic error surfaces were multiplied with a constant so that the surfaces closely represent the perceived uncertainty captured in these drilled wells. Seismic variance attribute was used to capture the uncertainty in fault zone. Variance was extracted on Mauddud surface from the variance cube generated. This variance surface was normalized with minimum and maximum values 1 and 6 respectively to use it as a multiplier to the stochastic error surfaces. The assumption was that the uncertainty will increase six times where there is maximum variance. The stochastic error surfaces were multiplied by the normalized variance surface to get the composite uncertainty. This uncertainty model was used to predict the uncertainty of Mauddud top in some wells drilled subsequently. The actual tops were found to be within the P10-P90 range except for a graben well where it was beyond the range. This study thus provided a model to quantify the range of uncertainty in predicting tops taking into account both distance from control points and uncertainty associated with fault zones as captured by seismic variance. Introduction Prediction of formation tops in wells prior to drilling is always associated with some amount of uncertainty. It happens due to uncertainty in our understanding and description of the reservoir. As the formation top depth is often related to expected oil column, assuming the depth of oil water interface is known, it is important to have a feel of the uncertainty at the time of selecting locations to be drilled. Also, knowledge of structural uncertainty helps the drilling engineer in designing the well and planning material requirement. This study analyses two important sources of structural uncertainty. The first one is distance from the drilled wells. As you go farther from drilled locations the uncertainty increases smoothly away from the well. The other one is inherent structural variance of the surface concerned. The uncertainty is more in places where the surface is dipping at a steeper inclination or in places affected by faulting. This aspect of structural uncertainty is captured by seismic variance attribute. The study integrates these two components to generate a composite uncertainty multiplier that would give a quantitative measure of uncertainty over the entire study area.
- Asia > Middle East > Kuwait > Ahmadi Governorate > Arabian Basin > Widyan Basin > Greater Burgan Field > Wara Formation (0.99)
- Asia > Middle East > Kuwait > Ahmadi Governorate > Arabian Basin > Widyan Basin > Greater Burgan Field > Ratawi Formation (0.99)
- Asia > Middle East > Kuwait > Ahmadi Governorate > Arabian Basin > Widyan Basin > Greater Burgan Field > Mauddud Formation (0.99)
- (3 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (0.68)
- Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (0.45)
- Management > Professionalism, Training, and Education > Communities of practice (0.41)
- Data Science & Engineering Analytics > Information Management and Systems > Knowledge management (0.41)
In Figure M-1, discuss how the symmetric wavelet is rotated while it is framed within the envelope. Take the simplest possible case, the case of three traces at one time level. Only one value is used on each trace -- say, the value at time t. These values of the three traces are as shown in Table M-1. In analysis of variance, we compute the average -- namely, ( 1 1 4) / 3 6 / 3 2 {\displaystyle \left({1\ \ 1\ 4}\right){/3} {6/3} {2}} . Then we subtract this average value from each of the three values to give the table of deviations shown in Table M-2.
- Information Technology > Knowledge Management (0.40)
- Information Technology > Communications > Collaboration (0.40)