Impact Of Geological Uncertainty Research Articles

Optimisation of long-term quarry production scheduling under geological uncertainty to supply raw materials to a cement plant

ABSTRACT The success of a cement production project depends on the supply of raw materials. Long-term quarry production scheduling (LTQPS) based on resource models is essential to maintain a consistent supply to cement plants. Geological uncertainty is inherent due to sparse exploration data in resource models and significant risk factors for not achieving production targets. This research proposes a stochastic framework for LTQPS that considers the impact of geological uncertainty on raw material supply. A clustering algorithm uses multiple simulated deposit models to aggregate blocks into mining cuts. A new stochastic mixed-integer programming model is formulated with two objectives: to minimise the cost for developing the raw mix and the risk of not meeting production targets. The proposed framework is implemented successfully in a limestone deposit in Southern Vietnam, resulting in an increase of 5 million tons (Mt) and a 30% reduction in unit cost over the deterministic mixed-integer programming model.

Impacts of geological store uncertainties on the design and operation of flexible CCS offshore pipeline infrastructure

Planning for Carbon Capture and Storage (CCS) infrastructure needs to address the impact of store uncertainties and store flow variability on infrastructure costs and availability. Key geological storage properties (pressure, temperature, depth and permeability) can affect injectivity and lead to variations in CO2 flow, which feed back into the pipeline transportation system. In previous storage models, the interface between the reservoir performance and the transportation infrastructure is unclear and the models are unable to provide details for flow and pressure management within a transportation network in response to changes in the operation of storage sites. Variation in storage demand due to daily and seasonal variations of fossil fuels uses and by extension CO2 flow is also likely to influence transportation infrastructure availability and the capacity to deliver. This work evaluates, at the level of infrastructure planning, the impact of geological uncertainty on CCS pipeline transportation and injection infrastructure. The analysis presented shows how to consider uncertainty in store properties in combination with CO2 flow variability to estimate the likely impact on pipeline infrastructure design. The operational envelope of the storage site infrastructure is estimated by combining the Darcy flow analysis of simple reservoir models with rigorous process simulation of the storage site wells. The proximity of wellhead conditions to the CO2 equilibrium line and the maximum velocities inside the well constrain the operational envelope of the storage site and limit the ability of the storage site infrastructure to handle CO2 flow variation. These factors, which are significantly influenced by variations in subsurface conditions, have also an impact on the design of the offshore pipeline infrastructure, needing to accommodate changes in pressure delivery requirements. Based on the evaluation of examples developed for different offshore transportation scenarios relevant to the United Kingdom, detailed insight on the expected impacts of store properties on pipeline transportation infrastructure design and operation is provided. For instance, it is found that enabling storage site flexibility is simpler in stores with an initial pressure above 20MPa. Given reductions in reservoir permeability, the requirements for pressure delivery are strongly dependent on the store temperature. Although the analysis is performed for specific geological characteristics in the North Sea the evaluation methodology is transferable to other locations and can be used for site screening to identify sites which are more flexible in terms of uncertainty in store performance.

Simulation Analysis With Association-Rule Mining Plus High-Dimensional Visualization

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 174774, “Using Association Rule Mining and High-Dimensional Visualization To Explore the Impact of Geological Features on Dynamic Flow Behavior,” by Satomi Suzuki, SPE, and Dave Stern, SPE, ExxonMobil Upstream Research Company, and Tom Manzocchi, SPE, University College Dublin, prepared for the 2015 SPE Annual Technical Conference and Exhibition, Houston, 28–30 September. The paper has not been peer reviewed. Computational advances in reservoir simulation have made possible the simulation of thousands of reservoir cases in a practical time frame. This opens a new avenue to reservoir-simulation studies, enabling exhaustive exploration of subsurface uncertainty and development/depletion options. However, analyzing the results of a large number of simulation cases remains challenging. This paper presents a new method that enables the efficient analysis of massive reservoir-simulation results by discovering interesting patterns of relationships among variables in large data sets. The method uses association-rule mining together with high-dimensional visualization. Introduction Ensemble-based approaches for reservoir modeling and simulation have been investigated for decades. The majority of the methods are designed to explore a high-dimensional space spanned by uncertainty parameters and find a set of reservoir models that reproduce historical production performance. Once an ensemble of history-matched models is obtained, the effect of subsurface uncertainty on production forecast is evaluated quantitatively by simulating flow performance on individual members of the model ensemble. Exploration of the parameter space during the model calibration is conducted with various stochastic algorithms. Major research efforts on these approaches are devoted to achieving efficient sampling from the parameter space and obtaining predictive capability to capture the range of uncertainty related to geological uncertainty remaining after data assimilation. However, little attention has been paid to qualitative analysis of the effect of geological features on simulated production performance. One obvious reason for the underutilization of the model ensemble for understanding reservoir sensitivity is the lack of efficient methods to visualize simulation results in such a way that interaction among multiple uncertainty parameters and simulated production response can be revealed rapidly. This paper presents a novel methodology that serves this purpose by coupling a well-known data-mining algorithm, called association-rule mining, with an existing high- dimensional visualization method called dimensional stacking. Data Set The Sensitivity Analysis of the Impact of Geological Uncertainty on Production (SAIGUP) project was an interdisciplinary reservoir-modeling project conducted from 2000 to 2004. The project aimed at studying the influence of geology on oil recovery from progradational shallow-marine reservoirs, which represent North Sea assets, by investigating peripheral-water-injection performances simulated on synthetic reservoir cases. The synthetic reservoir models built and simulated during the course of project add up to more than 35,000.

Multi-objective optimization for rapid and robust optimal oilfield development under geological uncertainty

Optimal well placement is critical to oil and gas field development. Typical workflows involve procedures to place a new well or a group of new wells in a reservoir in order to maximize some pre-defined reservoir performance metric. However, there are two main drawbacks with these traditional optimization approaches: First, the impact of geological uncertainty is often neglected or there may be no framework to include geological uncertainty. Second, traditional optimization techniques normally cannot meet the requirement of optimizing two or more conflicting objectives simultaneously—this may be useful when maximizing oil recovery while also minimizing water production. Consequently, in recent years, multiple objective optimization to obtain robust solutions that minimize the decision risk under geological uncertainty becomes a topic of renewed interest. Therefore, in this work, we develop a new work flow for well placement optimization while considering geological uncertainty in reservoir models. In general, when considering geological uncertainty, the primary goal is to maximize the mean net present value (NPV) over all realizations. However, restricting the search to simply maximizing the mean NPV may be inappropriate or inadequate for decision-making. A more reasonable choice is to maximize the mean NPV while minimizing the spread of the optimal NPV’s obtained for each realization. Therefore, in this work, we apply multi-objective optimization techniques to maximize the mean and minimize the variance of NPV values over all geological realizations to provide robust well placement solutions for decision-makers to select according to their risk attitude towards field development plans.

A Weighted Ensemble Kalman Filter for Automatic History Matching

The ensemble Kalman filter (EnKF) performs the initial sampling, forecasting, and assimilation steps for automatic history matching in the petroleum industry. It tunes multiple members sequentially and updates the statistical mean and variance of the model. Many applications have been reported in various publications. The forecasting step is implemented by running the reservoir model simulator. In the assimilation equation, the ensemble mean is calculated through equally weighting all the members. Therefore, the contribution factor to the mean from each member is the same. This paper proposes a modified assimilation equation by introducing a weighting factor for each ensemble member. Both the proposed weighted EnKF and the traditional EnKF are applied to a modified field case of a complex seventeen-layer reservoir. The performances of the weighted EnKF on production history match, forecasting, and field permeability match are better than those from the traditional EnKF. In addition, we investigate the impact of geological uncertainty in the initial ensemble generation on the final matching results. Two scenarios which have the same semivariogram as the reference field are implemented, and their results show that the initial geological information is important to the history matching performance.