306 THE LEADING EDGE December 2017 Waveform inversion of poststacked reflection seismic data with well log constrained using nonlinear optimization methods Abstract Surface refection seismic inversion techniques are currently applied by the industry for mapping the rock physical properties of oil reservoirs. Tis information permits speeding up the interpretation process to ultimately provide well locations. At present, many companies require having the inversion completed before any well is drilled. Inversion techniques can be applied to prestacked and poststacked seismic data. Prestacked data inversion is more complex than poststacked, but it provides more information for the interpreter (e.g., P-wave and S-wave imped- ances). On the other hand, poststacked inversion provides only the acoustic P-wave impedance. However, the main outcome of prestacked inversion is the increase in resolution when full waveforms are inverted. Currently, poststacked seismic inversion is used to correlate P-wave impedance with rock physical proper- ties obtained from well logs. Te logs are provided by a well near the survey line, allowing images of di ferent rock properties to be processed and analyzed. We extend the use of the acoustic P-wave impedance by constraining it with the well lithology, consequently categorizing the impedance by classes (i.e., sand, shale, and limestone) and converting the impedance to earth properties using well logs and regression models. Tis process allows us to build a single initial estimate of the earth property model, which is iteratively refned to produce a synthetic seis- mogram (by means of forward modeling) to match the observed seismic data. Te inversion algorithm that minimizes the misfts between observed and synthetic full-waveform data improves the P-wave velocity resolution. Te interpreter can thus delineate thin channels (fow units saturated with hydrocarbons) that are undetected using current techniques. Introduction We use well-log data and seismic lines from the Waggoner oil reservoir located in northeast Texas. At the Waggoner Ranch, independent and major oil companies have drilled and produced more than 200 million bbl in sandstone and carbonate reservoirs. All the production has been located within the gathering of rocks associated with the Red River Carbonate Platform in Wichita and Wilbarger counties. Most of the wells are about 800 m deep, frequently intercepting multiple pay zones and thinner limestone formations that are used as mark- ers. An important productive pay horizon is the top of the Milham Sands. Te sands are part of an alternating episode of transgression and regression of the paleoshoreline. Tus, the reservoir is considered a sand-shale sequence with limestone markers observed at large depth intervals. Tis sequence was characterized at the core and borehole scales by Parra et al. (2006). Results of the rock physics and fuid properties of the Jorge O. Parra 1 , Jonathan S. Parra 2 , and Ursula Iturraran-Viveros 3 1 SwRI. 2 Consultant. 3 UNAM. https://doi.org/10.1190/tle36120306.1. Figure 1. Lithologic section of well 3. Green is shale, yellow is sand, and blue is limestone. reservoir are given by Parra et al. (2015). Tis study demon- strated that attenuation logs detected oil-saturated sand using a rock physical model based on the wave-induced fow phe- nomena concept. Te analysis indicated that the top of the Milham Sands is a fow unit that is partially saturated with hydrocarbons at the borehole scale. In this article, we demonstrate a wave inversion algorithm that allows us to map thin pay zones at the seismic scale. Te algorithm is applied to delineate the Milham Sands at the Waggoner Reservoir. Data analysis We selected crossline 1176 and inline 1384 seismograms from a 3D seismic survey to dem- onstrate the inversion method. A new well (well 3) is located at the intersection of seismic lines 1176 and 1384. In this well, a complete suite of logs was acquired, including P- and S-wave monopole sonic and dipole sonic. A lithologic depth interval between 300– 770 m of well 3 is dis- played in Figure 1. Tis interval was obtained from Parra et al. (2006). We used the data from well 3 to produce scatter plots of velocity and density versus well-log impedance for sand, shale, and limestone. Scatter plots for the P-wave velocity are displayed in Figure 2 with regression lines for the sand and shale. Te best ft of the regression line for the sand is indi- cated by R 2 =0.95 and for the shale is specifed by R 2 = 0.96.