Horizontal wells dominate the development of unconventional shale reservoirs. Using real time drilling data to steer in a target zone is the key to economic success. Today structural interpretation in unconventional horizontal wells is a manual process that is time-consuming, tedious, and error-prone, especially because gamma-ray (GR) logs are commonly the only available logging-while-drilling data. For the first time, a method named TST3D is developed to automate interpretation of subsurface structure. TST3D (true stratigraphic thickness in three-dimensional space) automates structural interpretation using pattern recognition. Given an initial structural model, TST3D automatically computes true stratigraphic thickness (TST) as the shortest distance from each wellbore survey location to the initial surface, then matches GR patterns in the horizontal well to those seen in a vertical pilot well in TST domain. TST3D inserts fold hinges, bends the structure, then recomputes the modeled GR response, progressively matching the pilot well log signature, from heel to toe in the horizontal well. There are three assumptions in the current version of TST3D: constant layer thickness across the drilled interval, GR variation follows stratigraphic layering, and no faults are present in the drilled section. Those assumptions are reasonable in most shale plays. The TST3D method can be applied in either a post-drill mode for structural interpretation or real-time mode for aiding geosteering. Field tests in different shale plays and complex well trajectories demonstrate that TST3D runs quickly: a structural model of a 10,000-ft horizontal section can be computed in minutes, and a real-time update of 100 ft of new data takes less than a minute. Automating the geosteering correlation process would allow well placement engineers to cover multiple wells simultaneously, increasing the efficiency of the team while potentially improving service quality.
相似文献 |
|||||||
|
|||||||
|
排序方式： 共有10条查询结果，搜索用时 31 毫秒
1
1.
2.
在储层建模中利用多点地质统计学整合地质概念模型及其解释(英文) 总被引：1，自引：0，他引：1
基于三维空间中稀疏的观测数据,地质学家和储层建模人员尝试预测井间的地质沉积相的空间非均质性时,地质概念模型和先验认识在其中扮演着重要的角色。这种整合先验模型或解释的过程有时是隐蔽或不易察觉的,正如在手工绘等值线图中的情形;它也能够被显式地运用到某种算法当中,比如数字绘图中的算法。新近兴起的多点地质统计学为地质学家和储层建模人员提供了一种有力工具,它强调使用训练图像把先验模型明确而定量地引入到储层建模当中。先验地质模型包含了被研究的真实储层中确信存在的样式,而训练图像则是该模型的定量化表达。通过再现高阶统计量,多点算法能够从训练图像中捕捉复杂的(非线性)特征样式并把它们锚定到观测的井位数据。文中描述了多点地质统计学原理,以突出训练图像概念重要性为主线,描述了多点地质统计学在建立三维储层模型中的应用。 相似文献
3.
4.
Multiple-point simulation, as opposed to simulation one point at a time, operates at the pattern level using a priori structural information. To reduce the dimensionality of the space of patterns we propose a multi-point filtersim algorithm that classifies structural patterns using selected filter statistics. The pattern filter statistics are specific linear combinations of pattern pixel values that represent directional mean, gradient, and curvature properties. Simulation proceeds by sampling from pattern classes selected by conditioning data. 相似文献
5.
The Necessity of a Multiple-Point Prior Model 总被引：9，自引：0，他引：9
Any interpolation, any hand contouring or digital drawing of a map or a numerical model necessarily calls for a prior model
of the multiple-point statistics that link together the data to the unsampled nodes, then these unsampled nodes together.
That prior model can be implicit, poorly defined as in hand contouring; it can be explicit through an algorithm as in digital
mapping. The multiple-point statistics involved go well beyond single-point histogram and two-point covariance models; the
challenge is to define algorithms that can control more of such statistics, particularly those that impact most the utilization
of the resulting maps beyond their visual appearance. The newly introduced multiple-point simulation (mps) algorithms borrow
the high order statistics from a visually and statistically explicit model, a training image. It is shown that mps can simulate
realizations with high entropy character as well as traditional Gaussian-based algorithms, while offering the flexibility
of considering alternative training images with various levels of low entropy (organized) structures. The impact on flow performance
(spatial connectivity) of choosing a wrong training image among many sharing the same histogram and variogram is demonstrated. 相似文献
6.
Fast FILTERSIM Simulation with Score-based Distance 总被引：5，自引：3，他引：2
FILTERSIM is a pattern-based multiple-point geostatistical algorithm for modeling both continuous and categorical variables.
It first groups all the patterns from a training image into a set of pattern classes using their filter scores. At each simulation
location, FILTERSIM identifies the training pattern class closest to the local conditioning data event, then samples a training
pattern from that prototype class and pastes it onto the simulation grid. In the original FILTERSIM algorithm, the selection
of the closest pattern class is based on the pixel-wise distance between the prototype of each training pattern class and
the local conditioning data event. Hence, FILTERSIM is computationally intensive for 3D simulations, especially with a large
and pattern-rich training image. In this paper, a novel approach is proposed to accelerate the simulation process by replacing
that pixel-wise distance calculation with a filter score comparison, which is the difference between the filter score of local
conditioning data event and that of each pattern prototype. This score-based distance calculation significantly reduces the
CPU consumption due to the tremendous data dimension reduction. The results show that this new score based-distance calculation
can speed up FILTERSIM simulation by a factor up to 10 in 3D applications. 相似文献
7.
MPS-Driven Digital Rock Modeling and Upscaling 总被引：1，自引：0，他引：1
Tuanfeng Zhang 《Mathematical Geosciences》2015,47(8):937-954
8.
Memory-Efficient Categorical Multi-point Statistics Algorithms Based on Compact Search Trees 总被引：1，自引：1，他引：0
Tuanfeng Zhang Stein Inge Pedersen Christen Knudby David McCormick 《Mathematical Geosciences》2012,44(7):863-879
Multi-point statistics (MPS) has emerged as an advanced geomodeling approach. A practical MPS algorithm named snesim (simple normal equations simulation), which uses categorical-variable training images, was proposed in 2001. The snesim algorithm generates a search tree to store the occurrence statistics of all patterns in the training image within a given set of search templates before the simulation proceeds. The snesim search tree concept makes MPS simulation central processing unit efficient but consumes large amounts of memory, particularly when three-dimensional training images contain complex patterns and when a large search template is required to ensure optimal reproduction of the image patterns. To crack the memory-restriction bottleneck, we have developed a compact search tree that contains the same information but reduces memory cost by one order of magnitude. Furthermore, the compact structure also accelerates MPS simulation significantly. Such remarkable improvement makes MPS a more practical tool to use in building the large and complex three-dimensional facies models required in the oil and gas industry. 相似文献
9.
Zhang Tuanfeng McCormick David Nandlal Allan Lefranc Marie 《Mathematical Geosciences》2021,53(5):925-944
10.
1
|
| 设为首页 | 免责声明 | 关于勤云 | 加入收藏 |
|
Copyright©北京勤云科技发展有限公司 京ICP备09084417号 |