核磁共振（NMR）的纵向弛豫时间（T₁）、横向弛豫时间（T₂）、自扩散系数（D₀），以及T₂-T₁、T₂-D₀测量目前广泛应用于石油测井行业.在测量D₀的SGSE序列中，通过逐渐增大90°和180°脉冲之间的时间间隔（T_d），可以对液体扩散行为产生的影响进行调节.然而T_d的"起点"、"步进数"和"终点"等参数必须设置得当才能准确测量T₁和D₀.目前参数的设置依赖多次的人工调整和测量人员的经验，耗时且使用门槛较高.本文用蒙特卡罗方法进行大量随机模拟，根据前面若干点的测量结果筛选出满足要求的随机值，预测下一个测量点的位置.该算法可以实时更新参数设置，实现自动化测量，达到降低测量门槛、缩短测量时间的目的.经验证，该算法可以适用于T₁、D₀的测量.

Measurements of longitudinal relaxation time (T₁), transverse relaxation time (T₂), self-diffusion coefficient (D₀),T₂-T₁ and T₂-D₀ are central in NMR-based oil logging. The SGSE sequence is commonly used to measure D₀, in which the interval time between the 90° and 180° pulses (T_d) is increased incrementally to probe into the diffusion behaviors of liquids. However, the starting point, step size and end point of T_d must be properly set in order to get accurate measurement of T₁ and D₀. Currently, tuning of such parameters is often done manually, and thus time-consuming and difficult to use. The final outcome also relies heavily on operator's experience. In this study, a large number of random simulations were carried out by a Monte Carlo algorithm. The algorithm predicted the parameters for next measurement based on the results from previous measurements and thus was capable of updating the parameter settings in real time for automatic measurements. The algorithm was validated for T₁, D₀ measurements, and demonstrated reduced measurement threshold and shortened measurement time.