Optimization of nonconventional wells under uncertainty using statistical proxies

The determination of the optimal type and placement of a nonconventional well in a heterogeneous reservoir represents a challenging optimization problem. This determination is significantly more complicated if uncertainty in the reservoir geology is included in the optimization. In this study, a genetic algorithm is applied to optimize the deployment of nonconventional wells. Geological uncertainty is accounted for by optimizing over multiple reservoir models (realizations) subject to a prescribed risk attitude. To reduce the excessive computational requirements of the base method, a new statistical proxy (which provides fast estimates of the objective function) based on cluster analysis is introduced into the optimization process. This proxy provides an estimate of the cumulative distribution function (CDF) of the scenario performance, which enables the quantification of proxy uncertainty. Knowledge of the proxy-based performance estimate in conjunction with the proxy CDF enables the systematic selection of the most appropriate scenarios for full simulation. Application of the overall method for the optimization of monobore and dual-lateral well placement demonstrates the performance of the hybrid optimization procedure. Specifically, it is shown that by simulating only 10% or 20% of the scenarios (as determined by application of the proxy), optimization results very close to those achieved by simulating all cases are obtained.     

Optimization of SAGD Well Elevation

The application of steam-assisted gravity drainage (SAGD) to recover heavy oil sands is becoming increasingly important in the northern Alberta McMurray Formation because of the vast resources/reserves accessible with this mechanism. Selecting the stratigraphic elevations of SAGD well pairs is a vital decision for reservoir evaluation and planning. The inherent uncertainty in the distribution of geological variables significantly influences this decision. Geostatistical simulation is used to capture geological uncertainty, which is used can be transformed into a distribution of the best possible well pair elevations. A simple exhaustive calculation scheme is used to determine the optimum stratigraphic location of a SAGD well pair where the recovery R is maximized. There are three basic steps to the methodology: (1) model the uncertainty in the top continuous bitumen (TCB) and bottom continuous bitumen (BCB) surfaces, (2) calculate the recovery at all possible elevation increments within the TCB and BCB interval, and (3) identify the elevation that maximizes R. This is repeated for multiple TCB/BCB pairs of surfaces to assess uncertainty. The methodology is described and implemented on a subset of data from the Athabasca Oilsands in Fort McMurray, Alberta.     

Drainage spacing regularity on a fault-block: A case study from the eastern Ruahine Range

Abstract:  Recent research has indicated river basin outlets draining linear sections of large, uplifting mountain belts often show a regularity of spacing, transverse to the main structural trend. A morphometric analysis of part of the Ruahine Range, on the North Island was undertaken to test whether drainage regularity may exist in smaller, younger mountain ranges. The ratio, R , of the half-width of the mountain belt, W , and the outlet spacing, S , was used to characterize drainage networks on the eastern side of the range. The spacing ratio for the range of 1.31 is lower than R results from studies of larger mountain belts ( R  = 1.91–2.23). We suggest the cause of this lower ratio is related to eastward migration of the Ruahine drainage divide.     

Electromagnetic methods to characterize the Savoia di Lucania waste dump (Southern Italy)

The aim of this work is the joint application and integration of non-invasive geoelectrical methods for studying the landfill of Savoia di Lucania (Southern Italy). This landfill for its engineering features and small dimensions (70 m × 30 m × 6 m) represents an optimal test site to assess a geophysical survey protocol for municipal solid waste landfills investigation and monitoring. The landfill of Savoia di Lucania has been built with a reinforced concrete material and coated with a high-density polyethylene (HDPE) liner. Three electrical resistivity tomographies (ERT), two self-potential (SP) map surveys and one induced polarization (IP) section have been performed, both in the surrounding area and inside the waste landfill. The geophysical investigations have well defined some buried boundaries of the landfill basin and localized the leachate accumulation zones inside the dumpsite. Comparison of our results with other engineering and geological investigations could be the key for evaluating the integrity of the HDPE liner. Finally, the joint use of the ERT, IP and SP methods seems to be a promising tool for studying and designing new monitoring systems able to perform a time-lapse analysis of waste landfill geometry and integrity.     

低阻油气储层测井解释方法研究

对低阻油气储层的测井响应特征和储层物性特征进行了研究,并针对研究区域低阻储层的特点,以岩心与测井数据的相关性为依据,提出了适合地区特点的储层参数解释方法。通过应用三水导电模型,提高了低阻储层的测井解释精度。     

视真电阻率ρz在定井位中的应用探讨

介绍了视真电阻率ρz计算方法，通过对实际测深资料与井中电测资料的对比，证明了视真电阻率ρz的可靠性。在实际应用中可取得良好的效果。     

旧供水井化学处理方法介绍--以加德满都市旧供水井修复为例

本文以加德满都市旧供水井修复项目为例,针对因滤水管缝隙被化学物质堵塞,导致井出水量逐年减少的旧供水井。通过井下电视摄像系统检查,采用六偏磷酸钠、氨基黄酸等有机酸,结合刷洗、高压喷射等机械方法进行处理,使旧供水井出水量得到明显提高。     

一种基于核学习的储集层渗透率预测新方法

基于核学习的支持向量机,是一种采用结构风险最小化原则代替传统经验风险最小化原则的新型统计学习方法,具有完备的理论基础。这里提出了核学习技术在储集层非均质特性描述中渗透率参数预测的新用途。在复杂地层中,基于支持向量机的智能和自适应模式识别能力而建立了常规测井多参数信息输入的渗透率预测模型,然后对实际油田储集层渗透率进行了预测。与常规线性回归模型预测结果相对比,所提出的方法更易于使用,很少受不确定因素的影响,并具有较强的信息整合能力以及更高的预测准确性和可信度。     

岩石复电阻率Dias模型及其参数求取方法

Dias模型是众多描述岩石复电阻率的频散特性模型中的一种,它不但参数少,且可由这些参数导出具有特定物理意义的参数。针对Dias模型参数的求取及反演解的不确定性评价,这里提出了一种模拟退火阻尼最小二乘联合反演方法。该方法首先将参数求解空间离散化,利用模拟退火找到全局最优解区间,然后利用阻尼最小二乘法在解空间进行求解。该方法有效地克服了原来两种方法的反演速度慢、反演结果依赖于初值的缺点,并实现了对反演结果多解性的评价。研究结果表明,对于岩石复电阻率数据的处理解释,Dias模型的使用比传统的Cole模型及其变形更具有潜在的优势。