One of the greatest technical challenges in the development of gas and oil unconventional reservoirs is the optimization of the horizontal and vertical well spacing. Drainage volume and depletion efficiency around a horizontal lateral are the most important factors in the well spacing decision-making process. Drainage volume around a horizontal well is dependent on the stimulation design/execution and on the petrophysical and textural properties of the reservoir in the near wellbore area. Surveillance data acquired during stimulation (microseismic monitoring and tracers data) and during production (well pressure & interference) are routinely used to estimate the expected drainage area around wellbores. These surveillance measurements have numerous technical and operational limitations such as availability and location of monitoring well, accuracy of velocity model, detection of radioactive tracers away from wellbore, temperature limitation for down hole pressure gauges, etc.
In this study, we show a workflow that can reduce the level of uncertainty in estimating drainage area and depletion zones around wellbores. The workflow consists of: 1) Converting microseismic events into a density volume. 2) Conditioning the density volume by adding surface seismic attributes in zones where higher permeability is to be expected. 3) Conversion of the conditioned density volumes to permeability using rate transient and stimulation drivers such chemical tracers and production logs. 4) The 3D permeability model is fed into a reservoir simulator to match pressure and production history.
Microseismic density volumes are predicted in adjacent boreholes at varying distances from the actual wellbore to create a multiwell density volume using surface seismic attributes and rock property estimates. These volumes are taken into a reservoir simulator to predict expected ultimate recovery, recovery factor and the stimulated area per well.