深层油气资源量巨大,是全球油气开发的重要方向.随着钻井朝着深层(>4500 m)和超深层(>6000 m)发展,地质条件更加复杂,深层钻井泥浆信号传输速率受限,井下随钻测井等数据传输延迟,增加了钻井事故的频率及钻出储层的风险.当前井场...深层油气资源量巨大,是全球油气开发的重要方向.随着钻井朝着深层(>4500 m)和超深层(>6000 m)发展,地质条件更加复杂,深层钻井泥浆信号传输速率受限,井下随钻测井等数据传输延迟,增加了钻井事故的频率及钻出储层的风险.当前井场智能决策钻井的方法不适用,井下自主智能钻进是未来深层超深层高效钻进的发展方向.本文借鉴无人驾驶汽车的理论技术架构,提出了一种大闭环伺服控制随钻智能导向钻井方法,集旋转导向、地质导向、随钻地震、电磁前探、随钻测量、信号传输、自动钻机等技术于一体,利用“边钻边学”的人工智能评价与决策方法,智能识别钻头前方油气藏甜点,智能决策钻进方向和钻速,并利用大闭环伺服控制实现井下钻头的自主智能导向和钻进.大闭环伺服控制随钻智能导向钻井架构包括钻进感知、智能决策与大闭环控制3个部分.钻进感知部分通过随钻测井数据获取钻头定位信息、井周地层及钻头前方特性参数,智能决策部分依据钻进感知部分获取的信息通过人工智能决策模型修正轨道和优化钻进策略,大闭环控制部分根据智能决策指令调整钻进方向和速度.本文在钻进感知部分采用支持向量机算法利用随钻测井数据进行岩性智能识别,优选随机森林算法和长短期记忆(Long Short Term Memory,LSTM)循环神经网络对孔隙度、渗透率、饱和度和泥质含量进行评价.在智能决策部分优选随机森林算法对机械钻速进行预测与优化,均获得了高准确率.展开更多
At present, mud pulse transmission systems are widely used in downhole data transmission. But the systems are very low in transmission efficiency, only 5-10 bits/s, with very large anti-inter-symbol-interference (ISI)...At present, mud pulse transmission systems are widely used in downhole data transmission. But the systems are very low in transmission efficiency, only 5-10 bits/s, with very large anti-inter-symbol-interference (ISI). It cannot meet high requirements for high-speed transmission of modern logging system. The development of communication technology has laid some foundation for this requirement. For this purpose, the Orthogonal Frequency Division Multiplexing (OFDM) Wireless Downhole Transmission Systems are proposed for the first time because of their high transmission rate, anti-inter-symbol-interference (ISI), and high spectral efficiency, etc. Due to non-linear power amplifier (PA) of logging systems with limited dynamic range, the drawbacks of high peak-average power ratio (PAPR) may outweigh all the potential benefits of OFDM wireless downhole transmission systems. Selective mapping (SLM) method can reduce the PAPR of OFDM logging signals without distortion. But at the receiver, the conventional SLM method needs exact bits of side information (SI) to recover the data signal. The probability of erroneous SI detection has a significant influence on the error performance of the system. And individual transmissions of SI result in the reduction of bandwidth efficiency. To restore the exact data signal, our scheme codes the SI bits by linear block codes (LBC), and is easily decoded by syndrome decoding. And then the coding SI bits are superimposed onto the logging signals to omit SI bits transmission. The theory and simulation results show that the proposed method has better performance than the conventional one. Accordingly, the OFDM wireless downhole transmission systems can tackle the high PAPR problem, and highten the transmission rate of logging signals.展开更多
文摘深层油气资源量巨大,是全球油气开发的重要方向.随着钻井朝着深层(>4500 m)和超深层(>6000 m)发展,地质条件更加复杂,深层钻井泥浆信号传输速率受限,井下随钻测井等数据传输延迟,增加了钻井事故的频率及钻出储层的风险.当前井场智能决策钻井的方法不适用,井下自主智能钻进是未来深层超深层高效钻进的发展方向.本文借鉴无人驾驶汽车的理论技术架构,提出了一种大闭环伺服控制随钻智能导向钻井方法,集旋转导向、地质导向、随钻地震、电磁前探、随钻测量、信号传输、自动钻机等技术于一体,利用“边钻边学”的人工智能评价与决策方法,智能识别钻头前方油气藏甜点,智能决策钻进方向和钻速,并利用大闭环伺服控制实现井下钻头的自主智能导向和钻进.大闭环伺服控制随钻智能导向钻井架构包括钻进感知、智能决策与大闭环控制3个部分.钻进感知部分通过随钻测井数据获取钻头定位信息、井周地层及钻头前方特性参数,智能决策部分依据钻进感知部分获取的信息通过人工智能决策模型修正轨道和优化钻进策略,大闭环控制部分根据智能决策指令调整钻进方向和速度.本文在钻进感知部分采用支持向量机算法利用随钻测井数据进行岩性智能识别,优选随机森林算法和长短期记忆(Long Short Term Memory,LSTM)循环神经网络对孔隙度、渗透率、饱和度和泥质含量进行评价.在智能决策部分优选随机森林算法对机械钻速进行预测与优化,均获得了高准确率.
文摘At present, mud pulse transmission systems are widely used in downhole data transmission. But the systems are very low in transmission efficiency, only 5-10 bits/s, with very large anti-inter-symbol-interference (ISI). It cannot meet high requirements for high-speed transmission of modern logging system. The development of communication technology has laid some foundation for this requirement. For this purpose, the Orthogonal Frequency Division Multiplexing (OFDM) Wireless Downhole Transmission Systems are proposed for the first time because of their high transmission rate, anti-inter-symbol-interference (ISI), and high spectral efficiency, etc. Due to non-linear power amplifier (PA) of logging systems with limited dynamic range, the drawbacks of high peak-average power ratio (PAPR) may outweigh all the potential benefits of OFDM wireless downhole transmission systems. Selective mapping (SLM) method can reduce the PAPR of OFDM logging signals without distortion. But at the receiver, the conventional SLM method needs exact bits of side information (SI) to recover the data signal. The probability of erroneous SI detection has a significant influence on the error performance of the system. And individual transmissions of SI result in the reduction of bandwidth efficiency. To restore the exact data signal, our scheme codes the SI bits by linear block codes (LBC), and is easily decoded by syndrome decoding. And then the coding SI bits are superimposed onto the logging signals to omit SI bits transmission. The theory and simulation results show that the proposed method has better performance than the conventional one. Accordingly, the OFDM wireless downhole transmission systems can tackle the high PAPR problem, and highten the transmission rate of logging signals.
