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Search Chevron Energy Technology Company: Pages with reference errors
OWSG Standard Survey Tool Error Model Set for Improved Quality and Implementation in Directional Survey Management
Grindrod, S. J. (Copsegrove Developments Ltd) | Clark, P. J. (Chevron Energy Technology Company) | Lightfoot, J. D. (Occidental Petroleum Corporation) | Bergstrom, N.. (Devon Energy Corporation) | Grant, L. S. (Noble Energy, Inc.)
...d in other aspects of well field development and regulatory requirements. The systematic and random errors that contribute to the uncertainty of a measurement can be estimated to a high confidence. Generall...y errors considered are those that associated with running configurations, referencing and instrumentations...ve a material bearing on achieving well objectives. Additionally these models can also be used as a reference or baseline to evaluate custom models produced for specific survey tools or operations. Link to SPE...
...sed on the preceding selection(s) so the selection cascades through any of the following: Depth reference ¡Tool type ¡ Magnetic tool ¡ Declination source ¡ Corrections Depth ...reference ¡ Tool type ¡ Gyro tool Depth ...reference ¡ Tool type ¡ Gyro tool ¡ Gyro contractor Depth ...
...r a number of other geomagnetic models are in common use. These include High Resolution Geomagentic Reference Models (such as the HDGM or MVHD), however at least two other low definition geomagnetic models are...odels Taking into account all of the potential combinations of the corrections along with the depth reference constraint there are eight SPE-WPTS (ISCWSA) endorsed tool positional uncertainty models and all of...
Abstract Understanding wellbore position and the associated uncertainty is fundamental to all drilling operations and reservoir management. Without consistency in predicting to known uncertainties, activities involving positional uncertainty, such as risk mitigations for collision avoidance, cannot be performed reliably with known confidence. For the first time, industry has a common controlled set of uncertainty models thus allowing for transparency in error estimation. Reservoir targeting and subsurface hazard avoidance can be compromised resulting in unrealized stranded reserves and/or intersection of faulted, undesirable formation. Overly optimistic estimations can result in wellbore collisions where the risk of collision is assumed to be very low or can result in a missed well intersection during relief well first ranging point operation. Conversely, overly conservative estimations can result in excessive targeting constraints or directional control requirements. An analysis of industry survey of error codes being utilized across companies was performed, both vast inconsistencies and significant gaps were realized. A case for action was determined and a collaborative work group was formed under the Operator Wellbore Survey Group (OWSG). OWSG is a subcommittee of the SPE Wellbore Positioning Technical Section (SPE-WPTS). The SPE-WPTS originated as the Industry Steering Committee on Wellbore Surveying Accuracy (ISCWSA), which affiliated to the SPE and became a Technical Section. It was determined that many of the error codes being utilized in industry were based on survey tool error models established in SPE 67616 and SPE 90408, but there were uncontrolled changes, miss matched versions, and alterations in error assumptions (that were not vetted) being unknowingly utilized. Inspection revealed that the error models, although loosely based on ISCWSA, were varied across both service and operator companies and in some cases were varied internal to individual companies. The collaborative work group was established to develop a Standard Survey Tool Error Model Set based on the SPE 67616 and SPE 90408 publications, the current work of the ISCWSA error model subcommittee and with contributions from industry's leading subject matter experts. The result of the work is a series of 5 sets of OWSG Survey Tool Error Models. The sets have a Model Selection guide with a Standardized naming structure. The Standard Survey Tool Error Model Set has been released into the public domain to improve Survey Management Quality and has been adopted by numerous companies (both services and operators). The OWSG error model set facilitates easy implementation in all common directional well planning software platforms; hence reducing risk of incorrect models leading to poor understanding of wellbore position and improving consistency of error estimation. The Standard Survey Model set has been compiled with contributions from industry's leading subject matter experts. The most recent second revision (Rev2) of sets A and B of the series were publicly released during the month of June of 2015.
- Asia (0.68)
- North America > United States > Texas (0.46)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (0.55)
- Geophysics > Borehole Geophysics (0.55)
- Well Drilling > Wellbore Positioning (1.00)
- Well Drilling > Well Planning > Trajectory design (1.00)
- Well Drilling > Drilling Operations (1.00)
Multiscale Finite-Volume Formulation for the Saturation Equations
Zhou, H.. (Stanford University) | Lee, S.H.. H. (Chevron Energy Technology Company) | Tchelepi, H.A.. A. (Stanford University)
...operators to obtain the global solution. Aarnes (2004) proposed a Tchelepi (SPE Journal, June 2008, pages 267-273) is presented. modified mixed finite-element scheme, which constructs special Thus, the flo...l implicit solution, and the black-oil formulation. Zhou are in excellent agreement with fine-scale reference solutions, but and Tchelepi (2008) proposed an algebraic multiscale framework, at a much lower comp...
...port is summarized in Table 2. where the superscripts ms and f denote, respectively, multiscale and reference fine-scale quantities. Results Line Drive. We study a 2D problem in which the flow is driven In th...s demonstrated by comparing of the SPE 10 model (Christie and Blunt 2001). The variance of log with reference fine-scale solutions. The efficiency is shown 2 permeability is l nk 5.45. The fine-scale grid...We use 10 3 as the tolerance to detect whether The L 2 norms of pressure and saturation errors are written as the saturation field has stabilized in the region behind the front. ms f Figs. 4 a...
...lated velocity (AVFS). In regions without squares, direct Fig. 8 shows the pressure and saturation errors over the simulation interpolation of saturation (AS) is applied. run. Also shown in Fig. 8 are th...e in excellent agreement with fine-scale computation are 683 and 2,657. Therefore, the increase the reference. Both have an error of less than 2%. The saturation in iterations in the multiscale approach is les...
Summary Recent advances in multiscale methods have shown great promise in modeling multiphase flow in highly detailed heterogeneous domains. Existing multiscale methods, however, solve for the flow field (pressure and total velocity) only. Once the fine-scale flow field is reconstructed, the saturation equations are solved on the fine scale. With the efficiency in dealing with the flow equations greatly improved by multiscale formulations, solving the saturation equations on the fine scale becomes the relatively more expensive part. In this paper, we describe an adaptive multiscale finite-volume (MSFV) formulation for nonlinear transport (saturation) equations. A general algebraic multiscale formulation consistent with the operator-based framework proposed by Zhou and Tchelepi (SPE Journal, June 2008, pages 267–273) is presented. Thus, the flow and transport equations are solved in a unified multiscale framework. Two types of multiscale operators—namely, restriction and prolongation—are used to construct the multiscale saturation solution. The restriction operator is defined as the sum of the fine-scale transport equations in a coarse gridblock. Three adaptive prolongation operators are defined according to the local saturation history at a particular coarse block. The three operators have different computational complexities, and they are used adaptively in the course of a simulation run. When properly used, they yield excellent computational efficiency while preserving accuracy. This adaptive multiscale formulation has been tested using several challenging problems with strong heterogeneity, large buoyancy effects, and changes in the well operating conditions (e.g., switching injectors and producers during simulation). The results demonstrate that adaptive multiscale transport calculations are in excellent agreement with fine-scale reference solutions, but at a much lower computational cost.
Calibrating Field-Scale Uncertainties to Local Data: Is the Learning Being Overgeneralized?
He, Jincong (Chevron Energy Technology Company) | Reynolds, Albert C. (University of Tulsa) | Tanaka, Shusei (Chevron Energy Technology Company) | Wen, Xian-Huan (Chevron Energy Technology Company) | Kamath, Jairam (Chevron Energy Technology Company)
... correct for the modeling error. After the correction, the result matches almost perfectly with the reference solution from the fine uncertainty characterization (dashed blue line). Finally, the purple line sh...nal elements in Eq. 33 to correct for the modeling error. Ignoring the correlation between modeling errors of different data points has resulted in an overestimation of uncertainty reduction in this case. ...
...tainty reduction of field-scale objective functions. In this paper, we derived formulas to quantify errors in the posterior cumulative distribution functions (CDFs) of the objective functions resulting from... long variogram for the local variation and a short data-detection range. In addition, the modeling errors for different measurement data points can be highly correlated even when the measurement ...errors for these data are independent. To correct for this modeling error, analytical and empirical formul...
...H 5 high; M 5 middle; L 5 low. In practice, to account for the omission of local variations, large errors/tolerances (often much larger than that of the gauge accuracy or numerical error) are used for the ...tching process to dampen the uncertainty reduction and the update of the CDFs. The values for these errors/tolerances have a significant effect on the result but are often just heuristically specified accor...type of modeling error can be highly correlated for different data points even when the measurement errors for these data points are independent. A correction scheme is derived from the formula for modeling...
Summary A common pitfall in probabilistic history matching is omitting the local variation of spatial uncertainties and falsely generalizing the learning from local data to the entire field. This can lead to radical overestimation of uncertainty reduction and bad reservoir‐management decisions. In this paper, we propose a methodology to quantify and correct for the error that arises from the omission of local variation in probabilistic history matching. Most performance metrics in an oil field, such as the original oil in place (OOIP) and the estimated ultimate recovery (EUR), are field‐scale objective functions that depend on properties (e.g., porosity) over the entire field. On the other hand, many measurement data from wells [e.g., bottomhole pressure (BHP)] are mainly sensitive to the reservoir properties near the locations where they are measured, and thus they are susceptible to local variations of reservoir properties. Calibrating field‐scale objective functions to local well data without properly characterizing the local variation can overestimate the uncertainty reduction of field‐scale objective functions. In this paper, we derived formulas to quantify errors in the posterior cumulative distribution functions (CDFs) of the objective functions resulting from the omission of local variation. We also provide a way to correct for the error and to recover the true posterior CDFs. Through theoretical derivation, we show that the modeling error that arises from the omission of local variation is dependent on the magnitude of the global and local variations of the uncertain properties (e.g., porosity). The larger the local variation relative to the global variation, the larger the error in the estimated posterior distributions. The error also depends on the variogram of the local variation and the detection range of the data. The error is larger for cases with a long variogram for the local variation and a short data‐detection range. In addition, the modeling errors for different measurement data points can be highly correlated even when the measurement errors for these data are independent. To correct for this modeling error, analytical and empirical formulas are proposed that have been shown to greatly improve the accuracy of the posterior distributions in a number of cases. To the best of our knowledge, this is the first time that the modeling error from the omission of local variation in the probabilistic history‐matching process has been quantified and corrected. The methodology proposed could help improve the reliability of the result from probabilistic history matching.
- Europe (1.00)
- Asia (1.00)
- North America > United States > Texas (0.68)
- Asia > Kazakhstan > Mangystau Oblast > Precaspian Basin > Tengiz Field > Tengiz Formation (0.99)
- Asia > Kazakhstan > Mangystau Oblast > Precaspian Basin > Tengiz Field > Korolev Formation (0.99)