【摘要】：石油和天然氣素有工業(yè)血液之稱,是一個(gè)國家的重要戰(zhàn)略性資源。近年隨著未勘探的易開發(fā)區(qū)域逐漸減少,地球物理研究人員已將油氣勘探的重點(diǎn)目標(biāo)轉(zhuǎn)向?qū)夹g(shù)要求更高的隱藏性油氣藏和復(fù)雜構(gòu)造性油氣藏。目前單純的儲(chǔ)層彈性或物性參數(shù)反演已不能完全滿足油氣勘探的需要,而利用疊前地震資料開展高分辨率的儲(chǔ)層彈性與物性參數(shù)同步反演已成為學(xué)術(shù)界和工業(yè)界共同關(guān)注的熱點(diǎn)問題。本文在回顧地震資料高分辨率處理和地震反演的研究背景及意義的基礎(chǔ)上,總結(jié)歸納出了目前疊前地震反演中存在的兩個(gè)問題:1)如何獲得高分辨率的地震資料;2)如何實(shí)現(xiàn)高分辨率的疊前地震同步反演的確定性優(yōu)化方法。為解決以上兩個(gè)問題,本文分別做了如下創(chuàng)新工作:1、針對(duì)問題1),本文提出了一種基于BP人工神經(jīng)網(wǎng)絡(luò)的地震資料高分辨率處理方法。該方法利用BP人工神經(jīng)網(wǎng)絡(luò)建立井旁地震道記錄振幅譜與補(bǔ)償系數(shù)之間的非線性映射關(guān)系,進(jìn)而利用該關(guān)系計(jì)算出其它待補(bǔ)償?shù)卣鹩涗浾穹V的補(bǔ)償系數(shù),接著對(duì)補(bǔ)償系數(shù)進(jìn)行空間加權(quán)平滑和自適應(yīng)補(bǔ)償位置選擇處理,最后將其作用于振幅譜,得到補(bǔ)償后的高分辨率地震記錄。該方法相較于其它方法,同時(shí)采用了測(cè)井資料信息和地震資料信息,盡量避免了補(bǔ)償不足或補(bǔ)償過多的現(xiàn)象,增強(qiáng)了補(bǔ)償依據(jù)。2、針對(duì)問題2),本文提出了一種基于雙參數(shù)彈性速度模型的儲(chǔ)層彈性與物性參數(shù)疊前地震同步反演的確定性優(yōu)化方法。該方法利用雙參數(shù)彈性速度模型建立儲(chǔ)層彈性與物性參數(shù)之間的聯(lián)系,在貝葉斯反演框架下以儲(chǔ)層彈性與物性參數(shù)聯(lián)合的后驗(yàn)概率為目標(biāo)函數(shù),同時(shí)利用地震資料高分辨處理方法提升同步反演初值的分辨率,最后用自適應(yīng)變步長(zhǎng)優(yōu)化方法求解得到分辨率更高的儲(chǔ)層彈性和物性參數(shù)。一方面本方法采用雙參數(shù)彈性速度模型,與其它巖石物理模型相比,該模型能夠更好的建立彈性參數(shù)與孔隙形狀參數(shù)之間的聯(lián)系,有助于認(rèn)識(shí)孔隙形狀對(duì)儲(chǔ)層彈性性質(zhì)的影響;另一方面本方法采用確定性優(yōu)化方法構(gòu)建反演框架和求解,與隨機(jī)優(yōu)化方法相比,反演速度更快、精度更高。本文提出的方法均運(yùn)用實(shí)際工區(qū)數(shù)據(jù)進(jìn)行了驗(yàn)證,從驗(yàn)證效果來看,本文提出的地震資料高分辨率處理方法在提升地震資料分辨率上有明顯的效果;本文提出的同步反演的確定性優(yōu)化方法具有很好的收斂速度和穩(wěn)態(tài)效果,井曲線投上去后吻合度好,滿足疊前反演要求。
[Abstract]:Oil and natural gas have been known as industrial blood, is an important strategic resources of a country. In recent years, with the decrease of unexplored areas, geophysical researchers have turned the key targets of oil and gas exploration to hidden reservoirs and complex structural reservoirs with higher technical requirements. At present, the simple inversion of reservoir elastic or physical parameters can no longer fully meet the needs of oil and gas exploration. The simultaneous inversion of reservoir elastic and physical parameters with high resolution using prestack seismic data has become a hot issue in both academia and industry. On the basis of reviewing the research background and significance of high-resolution processing and seismic inversion of seismic data, This paper summarizes two problems existing in prestack seismic inversion: 1) how to obtain high resolution seismic data 2) how to realize the deterministic optimization method of high resolution prestack seismic synchronous inversion. In order to solve the above two problems, this paper proposes a high resolution processing method of seismic data based on BP artificial neural network. In this method, BP artificial neural network is used to establish the nonlinear mapping relationship between amplitude spectrum and compensation coefficient of seismic track records, and then the compensation coefficient of amplitude spectrum of other seismic records to be compensated is calculated. Then the compensation coefficients are processed by spatial weighting smoothing and adaptive compensation position selection. Finally, the compensated high resolution seismic records are obtained by applying them to the amplitude spectrum. Compared with other methods, this method uses logging and seismic information to avoid the phenomenon of insufficient compensation or too much compensation. This paper presents a deterministic optimization method for simultaneous inversion of reservoir elastic and physical parameters based on two-parameter elastic velocity model. In this method, the relationship between reservoir elasticity and physical parameters is established by using a two-parameter elastic velocity model. In the framework of Bayesian inversion, the posterior probability of the combination of reservoir elasticity and physical parameters is taken as the objective function. At the same time, the high resolution processing method of seismic data is used to improve the resolution of the initial value of synchronous inversion, and the adaptive variable step size optimization method is used to solve the reservoir elastic and physical parameters with higher resolution. On the one hand, the two-parameter elastic velocity model is used in this method. Compared with other rock physical models, this model can better establish the relationship between elastic parameters and pore shape parameters, and help to understand the effect of pore shape on the elastic properties of reservoir. On the other hand, the deterministic optimization method is used to construct the inversion framework and solve it. Compared with the stochastic optimization method, the inversion speed is faster and the precision is higher. The methods proposed in this paper are verified by using the actual work area data. From the result of verification, the high resolution processing method of seismic data presented in this paper has obvious effect on improving the resolution of seismic data. The deterministic optimization method for synchronous inversion presented in this paper has good convergence rate and steady state effect, and the well curve has good coincidence after being put into the well, which meets the requirements of prestack inversion.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：P618.13;P631.4
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本文編號(hào)：2133296