蘇里格氣田召51區(qū)塊自動(dòng)間開井油套壓AI時(shí)序預(yù)測研究

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中圖分類號(hào)：TP311.1 文獻(xiàn)標(biāo)志碼：A 文章編號(hào)：2095-2945（2025）18-0067-04

Abstract：Thewelheadcasing pressureofhighwater-containingtightsandstonegasreservoirinSuligeisanimportant indicatortoreflecttheproductionperformanceofgaswellandformationenergystatus.Inordertoaccuratelypredictthe changesofcasingpressure，ensureeficientproductionofgaswels，andavoidabnormalevents，historicaldataofcasing presure ofautomaticshut-inwelsin Block Zhao51of Sulige GasFieldareselected，andfiveindicatorsincludingRMSE，R， MAE，MBE，andMAPEarecomprehensivelyused toquantitivelyevaluate BP，GA-BP，PSO-BP，RBF，ELM，RF，SVM，CNN， LSTMtheperformanceofthesenineartificialintellgencemodelsinthisblock.Theresultsshowthat，inBlockZhao51，the optimalodelforAItimeseriespredictingoilpresureisRBF，andtheoptimalmodelforAItimeseriespredictingcasing presureisPSO-BP.Itcanpredictthechangeofcasingpressureingaswelsinthenext48hours，anditsgeneralization abilityis5times higherthanthatoftheRFmodelwithpoorcomprehensiveevaluationperformance.Theresearchresultsareof greatsignificanceforimprovingtheaplicabiltyoftecasingpressrepredictionmodelintheblockandgivingfullplaytothe productivity of gas wells.

KeyWords： RBF;PSO-BP;production system optimization; casing pressure prediction; AI timing

蘇里格氣田召51區(qū)塊目前有20口氣井配備自動(dòng)化間開設(shè)備，需進(jìn)一步向智能化轉(zhuǎn)型，結(jié)合人工智能技術(shù)優(yōu)化生產(chǎn)制度提高采收率。（剩余4119字）