Advances in Virtual Flow Metering Using Deep Composite Lstm-Autoencoder Network for Gas-Condensate Wells

J. Omeke
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Abstract

In terms of cost and execution time, data-driven Virtual Flow Meters (VFM) are alternative solutions to traditional well testing (WT) and physical multiphase flow meters (MPFM) for production rate determination which is needed for critical decisions by operators but faced with the challenge of low accuracy due to the transient and dynamic state of multiphase flow systems. Recently, some progress has been recorded by training steady state feed-forward neural networks to learn to approximate production rate based on certain number of input features (e.g., choke opening, pressure and temperature etc.) without any recursive feedback connection between the network outputs and inputs. This disconnection has impacted their accuracy. Dynamic artificial neural network, for example, the recurrent neural networks (RNN), e.g., LSTM has shown good performance as its architecture allows for the usage of data from the past time step to predict the current time step. Forecast accuracy for RNN are limited to short period of time due to their inherent vanishing gradient issues. While majority of VFM application have been developed for oil and gas systems, little or non is applied to gas condensate system. In this project, a sequence-to-sequence deep composite LSTM-Autoencoders was explored and used to demonstrate the ability of leveraging on its architecture to accurately predict multiphase flow rate for some wells in a gas condensate reservoir with highly dynamic multiphase flow phenomenon. A more complicated flow system was developed using a 3D compositional simulator to simulate, as close as possible, a realistic case of compositional reservoir. A single well was used to train the model and a blind test was ran on two other wells in same reservoir whose data are not part of the training set in order to predict their flow rate with accuracy. Based on the actual vs predicted results demonstrated, especially the blind test case, the feature extraction and encoding process of the trained LSTM-autoencoder was actually learning the physics of fluid flow and accurately passing the encoded results to the two decoders with very good output (training and testing mean square error are 0.02 and 0.05 respectively). The ability to leverage on some advanced artificial intelligence framework such as a composite LSTM-autoencoder has proven that it is possible to achieve the desired accuracy needed in data driven VFM to meet the requirement of low cost, low execution time and high accuracy. This project has also demonstrated the ability of the data driven model to learn the complex dynamics within the temporal ordering of input sequences of production data, with an internal memory adapted to remember or use information across long input sequences, hence, yield longer and reliable forecast, unlike other networks.
凝析气井深层复合lstm -自编码器网络虚拟流量计量研究进展
在成本和执行时间方面,数据驱动的虚拟流量计(VFM)是传统试井(WT)和物理多相流量计(MPFM)的替代方案,用于产量测定,这是运营商做出关键决策所需要的,但由于多相流系统的瞬态和动态状态,面临精度低的挑战。最近,通过训练稳态前馈神经网络,在网络输出和输入之间没有任何递归反馈连接的情况下,根据一定数量的输入特征(如扼流圈开度、压力和温度等)来学习近似产量,已经取得了一些进展。这种脱节影响了它们的准确性。动态人工神经网络,例如递归神经网络(RNN),例如LSTM,由于其架构允许使用过去时间步长的数据来预测当前时间步长,因此表现出良好的性能。由于其固有的梯度消失问题,RNN的预测精度限制在较短的时间内。虽然VFM的大部分应用已经开发用于油气系统,但很少或根本没有应用于凝析气系统。在这个项目中,研究人员探索了一种序列到序列的深层复合lstm - autoencoder,并使用它来证明利用其架构准确预测具有高度动态多相流现象的凝析气藏某些井的多相流量的能力。利用三维成分模拟器开发了一个更复杂的流动系统,以尽可能接近真实的成分油藏情况。为了准确预测井的流量,我们使用了一口井对模型进行了训练,并对同一储层的另外两口井进行了盲测,这两口井的数据不属于训练集。根据所展示的实际与预测结果,特别是盲测试用例,训练后的lstm自编码器的特征提取和编码过程实际上是学习流体流动的物理特性,并将编码结果准确地传递给两个解码器,输出效果非常好(训练和测试均方误差分别为0.02和0.05)。利用一些先进的人工智能框架(如复合lstm -自动编码器)的能力已经证明,在数据驱动的VFM中,可以实现所需的精度,以满足低成本、低执行时间和高精度的要求。该项目还展示了数据驱动模型在生产数据输入序列的时间顺序中学习复杂动态的能力,其内部存储器适应于记忆或使用跨长输入序列的信息,因此,与其他网络不同,可以产生更长更可靠的预测。
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