發(fā)布時間：2018-06-10 01:33

  本文選題：油田區(qū)塊 + 成本動因　； 參考：《中國石油大學(華東)》2014年碩士論文

【摘要】：隨著國內(nèi)油田開發(fā)的不斷深入,許多油田區(qū)塊進入開發(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自然遞減,油田區(qū)塊成本不斷攀升。目前區(qū)塊成本預測的主要做法是參照歷史水平并根據(jù)本年的計劃來進行預測,這種做法缺乏可靠性和準確性。因此,尋找適合油田實際情況并且預測精度較高的成本預測方法,對于促進區(qū)塊成本的控制和經(jīng)濟效益的提高具有重大意義�；谝陨媳尘�,本文對油田區(qū)塊成本預測方法進行探討。首先對油田區(qū)塊成本的構成及特性進行了分析,在此基礎之上,對成本動因進行了選擇,得出了基于成本動因的成本函數(shù),并建立了成本動因合并模型,為進行成本預測奠定了基礎。然后,結合油田區(qū)塊成本預測的要求和對成本預測方法的評價分析,從諸多方法中選擇了BP神經(jīng)網(wǎng)絡預測方法。最后,以油田區(qū)塊油氣提升系統(tǒng)成本預測為例,建立BP神經(jīng)網(wǎng)絡預測模型進行預測,并就預測結果與回歸預測法、指數(shù)平滑法、移動平均法進行了對比分析,得出BP神經(jīng)網(wǎng)絡預測有利于提高油田區(qū)塊成本的預測精確性。在具體的應用中,還應該加強成本動因數(shù)據(jù)的基礎工作,完善信息管理系統(tǒng),進一步提高油田區(qū)塊成本預測水平。
[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.
【學位授予單位】：中國石油大學(華東)
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F426.22;F406.7

【參考文獻】

相關期刊論文 前10條

1 劉華華;張靜;李德山;;基于GM-DEA對天然氣操作成本預測及投入產(chǎn)出優(yōu)化研究[J];科技和產(chǎn)業(yè);2012年04期

2 袁姝;胡宏濤;趙越;;貝葉斯正則化BP神經(jīng)網(wǎng)絡在油氣鉆井成本預測的應用[J];軟件導刊;2010年12期

3 霍江林;趙振智;;油田企業(yè)作業(yè)動因成本性態(tài)分析[J];財會月刊;2010年06期

4 劉天時;趙越;馬剛;;基于BP神經(jīng)網(wǎng)絡的油氣鉆井成本預測[J];西安石油大學學報(自然科學版);2010年01期

5 董尚斌;;石油操作成本預測實證研究[J];油氣田地面工程;2009年10期

6 周超;王秀芝;;組合預測在油氣操作成本預測中的應用[J];西南石油大學學報(社會科學版);2009年04期

7 李志學;蘆偉;;油田生產(chǎn)成本項目的聚類特征及其動因研究[J];國際石油經(jīng)濟;2009年04期

8 耿建軍;姜海燕;曹玲;;油田企業(yè)成本動因控制法應用探討[J];會計師;2009年02期

9 王志明;;Kolmogorov定理的一個變形[J];數(shù)學雜志;2008年04期

10 郭一鳴;;塔里木油田原油單位操作成本的灰色預測[J];吐哈油氣;2007年03期

相關碩士學位論文 前1條

1 徐洪德;新疆油田分公司油氣操作成本預測方法研究[D];西南石油學院;2003年

，

本文編號：2001516