1. Quantico Energy Solutions LLCConfidential1 DensityCompressionalShear Poisson’s Ratio Young’s Modulus Reservoir Quality TOCLithology Thermal Maturity Effective Porosity Fluid Saturation Matrix Permeability Reservoir Pressure Brittleness Minimum Horiz Stress Neutron GEOMECHANICS RESERVOIR QUALITY Drilling Dynamics Mud Logs, Limited MWD/LWD M EASUREMENTS C OLLECTED BY O PERATOR S YNTHETIC I NDICATORS G ENERATED

2. Rock mechanics, lithology and other critical indicators for every well Quantico Energy Solutions LLCConfidential2

3. Quantico Energy Solutions LLC Confidential 3 QES P ROCESS F LOW  Drilling data sent to QES  QES processes inputs in proprietary software  Synthetic logs are sent to operator Directional / MWD Depth Gamma Temp Inclination ResistivityIf available Drilling Dynamics WOB ROP Pump Output / Flow In Torque Differential Pressure Standpipe pressure Mud weight Other Mud Log Total GasIf available Gas componentsIf available Input Measurements QLog Shear Compressional Density QFrac Young’s(TS,RhoB) Poisson’s (VPVS) Brittleness (Y/P) Stress Engineered completion Output Logs Example: Drilling data for rock mechanics Seismic $$$ Seismic $$$ Drilling $ Drilling $ Logging $$$ Logging $$$ Stimulation $ Stimulation $ Production $$$ Production $$$

4. Deliverable products from drilling input data QLog™ - synthetic shear, compressional and density logs QFrac™ - derivative rock mechanics properties YM, PR, brittleness, stress ACCURACY: Prediction has a variation from measured log within ~5%, on par with conventional logging tools RELIABILITY: Less susceptible to washouts, tool failures and centralization issues, questionable sonic. Team experienced in constructing neural nets that are commercially robust and reliable ECONOMIC: Price comparison of 1/10th to 1/15 th NO RISK: No lost-in-hole risk Quantico Energy Solutions LLCConfidential4 Operator Name Well Name API #

5. QES Basin Model Expansion Quantico Energy Solutions LLCConfidential5 BAKKEN Three Forks & Middle Bakken model wells covering four counties EAGLE FORD Eagle Ford Oil window model Eagle Ford Gas window (developing) wells covering six counties PERMIAN BASIN Wolfcamp Delaware model Wolfcamp Midland (developing) wells covering five counties MARCELLUS/UTICA Marcellus/Utica model wells covering three counties Wood Ford Fayetteville Niobrara

6. Neural Net Training Process Quantico’s software models use proprietary weightings of several input measurements that vary from basin to basin Quantico Energy Solutions LLCConfidential6 Model Weight Input Measurement Shale Basin D ATA I NTAKE Receive and review data sets for common inputs across service companies. Ensure consistent quality of such datasets. T RAINING Demonstrate ability to predict DTS, DTC and RHOB on training wells. Review predictions against measured horizontal logs. B LIND T EST Predict DTS, DTC and RHOB on near-by wells not used in training the models. Review predictions against measured horizontal logs. E RROR R EDUCTION Systematically reduce mean squared errors relative to measured logs

7. Selected aspects of NN training Quantico Energy Solutions LLCConfidential7  Simulations of well logs used in training should exhibit reasonable accuracy. Otherwise, we know we are missing key input parameter or physics does not work.  QES internal software has flags for where simulations models may be used. Determined by max/mins of training logs – not by geography/distance  We evaluate different combinations of input parameters. In Eagleford model, ~50 cases analyzed, with 30+ sub-models built for each case. >24 hrs to run each model  In multiple blind tests, high fidelity overlays with: Conventional logs from Schlumberger (Thrubit) and Weatherford Halliburton pulsed neutron generated density logs (derived from two completely independent development efforts and training sets)  In the 5% of instances where the overlays did not match, the conventional logs were shown to be suspect in most of those cases

8. Automated Zoning Software - select accurate engineering indicators for optimal stage placement Traditional  Stress Poisson’s Ratio (VPVS), Over Burden, Pore Pressure  Young’s Modulus DTS, Rhob  Brittleness YM & PR (Rhob, DTC, DTS)  Shale Volume Gamma Ray Quantico Energy Solutions LLCConfidential8 Other methods with less error prone parameters  VPVS DTC & DTS  Acoustic Impedance (Compressive) Rhob & DTC (ZC = 283*Rhob/DTC)  Acoustic Impedance (Shear) Rhob & DTS (ZS = 283*Rhob/DTC  Rhob + VPVS Rhob, DTC, DTS Error Propagation If primary logs have errors of Rhob 2%, DTc 6% and DTs 6% then – YM 9%, PR 24%, Brit 48%

9. Data-driven solutions offer unique advantages Quantico Energy Solutions LLCConfidential9 Accuracy & reliability  Similar measurement accuracy as re-log with conventional tools  QLogs exhibit fewer issues with tool centralization, tool failures, washouts and questionable sonic quality  Demonstrated performance in multiple blind tests of accuracy comparable to conventional logging tools Data driven advantages  Economic solution for collecting wide-scale rock mechanics information  As more logs are added to training models, accuracy improves for all customers  Learning curve in setting up custom software for new regions Other potential areas to apply data analytics  Identify local relationships between drilling data and pumping data  Future indicators related to reservoir quality, production  Future indicators related to reservoir quality using VClay  New solutions around drilling and seismic  Other opportunities for mining data that operators already have….