Modeling and Inversion with Azimuthal Gamma Ray for a Better GeosteeringDecision-Making

摘要

Azimuthal gamma ray(GR)logging-while-drilling(LWD)tools have demonstrated great value forgeosteering applications in directional drilling.Their ability to indicate the relative stratigraphic positionof the drilling assembly can determine whether the well should be steered up or steered down to stay inthe target zone.However,this determination remains rather qualitative and largely depends on the userexperience,especially when the quality and amount of real-time data is limited by intrinsic statistical noise,telemetry bandwidth,rate of penetration(ROP),and other drilling conditions.In addition,the commonlyused geosteering modeling for azimuthal GR is geometry based only,without considering any measurementphysics.Thus,a new forward-modeling and inversion method has been developed to provide an optimizedpre-job planning and potentially quantitative real-time decision-making for more accurate geosteering withazimuthal GR.The geosteering question can be simplified mathematically to a prediction of separation betweenazimuthal GR curves when approaching or passing a bed boundary in a two-bed formation model.Separation will give an indication of a steering direction change,even in the simplest case of only up-anddown-facing GR curves.In this study,a method was developed to solve this question in seconds with onlytwo factors: measurement precision and front-to-back ratio.The theoretical up-and down-facing readingsof an azimuthal GR tool can be forward-modeled accurately from this ratio for any bed boundary changes.Combined with measurement precision,the counting statistics effects can be added to the model to mimicthe real-world log curves,and this for any pre-selected stratigraphic marker.The forward model results agree well with industrial standards of full Monte Carlo nuclear simulationand its deterministic nature allow it to run very fast.Thus,various scenarios can be evaluated quickly duringeither the pre-well phase or the operation.Detection limits achieved by any azimuthal GR tool in any givenscenario can be statistically predicted for various confidence levels(e.g.,95% possibility of up/down curveseparation).Thus,based on the detection limits,confidence level,and their variation with ROP,.etc.thedrilling and geosteering plan can be optimized to reach the best ROP confidently without compromisingthe steering capability.Also in real time,when a potential separation of up-and down-facing azimuthalGR curves appear on the log,inversion of this model can be carried out to derive the possibility that this separation truly reflects formation changes to offer some quantitative insight to make steering decisions.Inversion of the modeling also has the potential to help recover the true API values of the formation bedsand enhance the detection of the bed boundary positions.The novelty of this approach stems from the statistical nature of nuclear counting statistics and thederivation of front-to-back ratio.Front-to-back ratio,when properly defined,is a factor that fully representsthe measurement physics of the tool.In addition to the aforementioned applications,an overall coveragechart can be recalculated as a quick look-up reference to measure the effectiveness of azimuthal GR.Thechart reflects the detection limits that an azimuthal GR tool can resolve for geosteering in a 0-200-APIsampling space at a certain confidence level.Overall,the paper includes a detailed description of the modeland its inversion,applications,and example log demonstrations from early trials.