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題名：以委員會機器為基底的滲透率預測模式
作者：陳昶旭
作者(外文)：Chang-Hsu Chen
校院名稱：國立成功大學
系所名稱：資源工程學系碩博士班
指導教授：林再興
學位類別：博士
出版日期：2006
主題關鍵詞：保留法；交叉驗證；袋裝法；滲透率；類神經網路；經驗公式；井測；預測；分而治之；委員會機器；divide and conquer；bagging；cross-validation；holdout；prediction；empirical formula；well log；permeability；neural network；committee machine
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本研究目的為應用委員會機器(Committee Machine, CM)原理來發展新的滲透率預測模式，其中傳統的滲透率經驗公式及倒傳遞類神經網路(BPNN)皆被用來扮演委員會機器中的專家成員角色，發展新的滲透率預測模式；應用委員會機器原理及資料處理的統計技術所發展的滲透率預測模式，包括有CMEF模式(committee machine with empirical formulas, CM結合經驗式)、CNNH模式(committee neural network with holdout method, CNN結合保留法)、CNNKCV模式(CNN with K-fold cross-validation, CNN結合交叉驗證)、CNNB模式(CNN with bagging, CNN結合袋裝法)及CNNDC模式(CNN with divide and conquer, CNN結合分而治之法)。
在本研究中所有以CM為基底發展的模式，在利用井測資料及岩心資料，進行滲透率預測時，並不需要像傳統的滲透率經驗公式一樣，需要知道很明確的地層流體性質及岩石性質的詳細資料；此外，這些CM基底的滲透率模式，在進行預測時，都比傳統的經驗公式或單一的BPNN模式具有較佳的強健性(robust)及正確性(accurate)。
本研究應用所提出的CM滲透率模式成功地分析了一組現場資料，這些被用來當作模式輸入輸出的資料包括八種井測資料及相對應深度的岩心量測滲透率，八種井測包括井徑電測(CALI)、自然珈瑪電測(GR)、深感應式電阻電測(LLD)、微球聚式焦點電阻電測(MSFL)、淺感應式電阻電測(LLS)、聲波電測(DT)、密度電測(RHOB)、中子電測(NPHI)等。
本研究的分析結果中，CMEF模式結合CM的概念，將三個常用的滲透率經驗公式當作是預測專家，此模式展現的預測結果優於三種經驗公式的個別預測結果；CNNH模式利用不同的起始神經元連結值來訓練各個預測專家(BPNN)成員，而CNNKCV模式則利用不同組合的訓練資料集合來訓練各個預測專家(BPNN)成員，由最後預測的結果得知，CNNH模式及CNNKCV模式皆可獲得比單一BPNN模式更高的預測效能。
至於經過袋裝法(bagging)處理的CNNB模式，其判定係數遠比沒有進行bagging的模式為高，此結果充分展現了bagging技術的效果，此模式藉由bagging來擴大利用有限獲得的資料點，並提高了滲透率預測的效能。而CNNDC模式則把大範圍的滲透率分佈資料分割成低滲透率及高滲透率等兩個子範圍，然後對各自子範圍內的訓練集合資料點做訓練學習，並進行各自的解析預測，此模式不僅改善了滲透率預測的精確度，而且其概化(generalization)也獲得了較佳的結果。
This study aims to employ committee machines (CMs) to propose novel models for permeability estimation. In this study, conventional empirical formulas and backpropagation neural networks (BPNNs) were used as members of the CMs. These CM-based models were developed to improve prediction for formation permeability.
The proposed CM-based models included the CMEF model (CM with empirical formulas), CNNH model (committee neural network [CNN] with holdout method), CNNKCV model (CNN with K-fold cross-validation), CNNB model (CNN with bagging), and CNNDC model (CNN with divide and conquer).
In this study, all the CM-based models were developed to predict the permeability directly from well logs without explicit knowledge of the fluid and rock properties. This study demonstrated that the CM-based models are more robust and accurate than the conventional empirical formulas and a single BPNN.
The CM-based models were successfully applied to analyze field data consisting of eight well logs and core-measured permeability. The eight logs used included Caliper (CALI), Gamma Ray (GR), Laterolog Deep Resistivity (LLD), Micro Spherically Focused Resistivity (MSFL), Laterolog Shallow Resistivity (LLS), Interval Transit Time (DT), Bulk Density (RHOB), and Thermal Neutron Porosity (NPHI) logs.
The CMEF model, combining outputs from three empirical formulas, was superior to the three individual empirical formulas acting alone. As for the CNNH and CNNKCV models, BPNNs in the CNNH model were trained by varying the initial weights, whereas BPNNs in the CNNKCV model were trained by varying the combined training data. The results from the CNNH and CNNKCV models demonstrated the power of CM by obtaining larger R2 value compared with a single BPNN.
The CNNB model was developed to leverage the limited core data pairs by applying the bagging (bootstrap aggregating) technique. This model demonstrated its effect with a small data set by obtaining a considerably larger R2 value compared with a model without bagging. The CNNDC model was provided to demonstrate the benefits of modularity by decomposing the permeability range into two sub-ranges to increase the resolution. This implemented model improved the accuracy for permeability prediction and also earned its expected result by achieving the best generalization.
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