發(fā)布時(shí)間：2018-06-10 01:33

  本文選題：油田區(qū)塊 + 成本動(dòng)因��； 參考：《中國(guó)石油大學(xué)(華東)》2014年碩士論文

【摘要】：隨著國(guó)內(nèi)油田開發(fā)的不斷深入,許多油田區(qū)塊進(jìn)入開發(fā)的中后期,含水率和開采難度不斷上升,為維持產(chǎn)量水平需要投入巨額成本來彌補(bǔ)自然遞減,油田區(qū)塊成本不斷攀升。目前區(qū)塊成本預(yù)測(cè)的主要做法是參照歷史水平并根據(jù)本年的計(jì)劃來進(jìn)行預(yù)測(cè),這種做法缺乏可靠性和準(zhǔn)確性。因此,尋找適合油田實(shí)際情況并且預(yù)測(cè)精度較高的成本預(yù)測(cè)方法,對(duì)于促進(jìn)區(qū)塊成本的控制和經(jīng)濟(jì)效益的提高具有重大意義�；�于以上背景,本文對(duì)油田區(qū)塊成本預(yù)測(cè)方法進(jìn)行探討。首先對(duì)油田區(qū)塊成本的構(gòu)成及特性進(jìn)行了分析,在此基礎(chǔ)之上,對(duì)成本動(dòng)因進(jìn)行了選擇,得出了基于成本動(dòng)因的成本函數(shù),并建立了成本動(dòng)因合并模型,為進(jìn)行成本預(yù)測(cè)奠定了基礎(chǔ)。然后,結(jié)合油田區(qū)塊成本預(yù)測(cè)的要求和對(duì)成本預(yù)測(cè)方法的評(píng)價(jià)分析,從諸多方法中選擇了BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)方法。最后,以油田區(qū)塊油氣提升系統(tǒng)成本預(yù)測(cè)為例,建立BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型進(jìn)行預(yù)測(cè),并就預(yù)測(cè)結(jié)果與回歸預(yù)測(cè)法、指數(shù)平滑法、移動(dòng)平均法進(jìn)行了對(duì)比分析,得出BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)有利于提高油田區(qū)塊成本的預(yù)測(cè)精確性。在具體的應(yīng)用中,還應(yīng)該加強(qiáng)成本動(dòng)因數(shù)據(jù)的基礎(chǔ)工作,完善信息管理系統(tǒng),進(jìn)一步提高油田區(qū)塊成本預(yù)測(cè)水平。
[Abstract]:With the deepening of domestic oilfield development, many oil field blocks enter the middle and late stage of development, the water cut and the difficulty of exploitation are rising constantly. In order to maintain the production level, it is necessary to invest a huge amount of cost to make up for the natural decline, and the block cost of the oil field is constantly rising. At present, the main method of block cost prediction is to forecast according to historical level and according to this year's plan, which lacks reliability and accuracy. Therefore, it is of great significance to find a cost forecasting method suitable for the actual situation of oil field and to improve the economic benefit and control of block cost. Based on the above background, this paper discusses the prediction method of oil field block cost. Firstly, the composition and characteristics of oil field block cost are analyzed, then the cost driver is selected, the cost function based on cost driver is obtained, and the cost driver combination model is established. It lays a foundation for cost prediction. Then, according to the requirements of oil field block cost prediction and the evaluation and analysis of cost forecasting methods, BP neural network forecasting method is selected from many methods. Finally, taking the cost prediction of oil and gas lifting system in oilfield block as an example, a BP neural network forecasting model is established, and the prediction results are compared with regression prediction method, exponential smoothing method and moving average method. It is concluded that BP neural network prediction is helpful to improve the accuracy of oil field block cost prediction. In the specific application, we should strengthen the basic work of cost driver data, perfect the information management system, and further improve the level of oil field block cost prediction.
【學(xué)位授予單位】：中國(guó)石油大學(xué)(華東)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F426.22;F406.7

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本文編號(hào)：2001516