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Optimization Work Flow Maximizes Value of Unconventional Fields

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Well spacing optimization is one of the more important considerations in unconventional field development. The essence of field development and optimization is to use completions design and well spacing to optimize the net present value (NPV) of the field on the basis of current commodity pricing, capital expenditure (CAPEX), operating cost, cycle time, and net revenue interest. A substantial variation in any of these essential factors must be studied to make sure the appropriate changes are accounted for in field development and optimization. A fast-paced and dynamic work flow has been developed that can be applied in different shale reservoirs to maximize the NPV of these assets. This paper describes the work flow, starting with a fracture model, then coupled with a production model using numerical simulation to obtain a calibrated model, and, finally, a detailed economic and sensitivity analysis to obtain the well spacing and completions design that will yield the highest NPV of the field.

Introduction

When well spacing systems (interlateral spacing) for various unconventional basins were developed, commodity pricing was much higher and completions job sizes were smaller than they are today. The majority of wells were completed with less than 1,300 lbm/ft of proppant. As operators increased job sizes and seized the benefit of higher production performance, discussions regarding increasing well spacing also took place.

After 2014, operators sought ways to stay economical at lower commodity pricing and began to consider feasible ways to reduce operational costs, improve well productivity, raise NPV/acre, automate processes and work flows, use machine learning (ML) to improve predictability, and optimize workforce efficiency. Optimal well spacing for any unconventional well depends on many factors, including gas price, capital and operating expense, acreage position and inventory, completions design, production performance, and lateral length. There is no one-size-fits-all well spacing for various completions designs. Performing a full analysis, therefore, is crucial to finding the optimal well spacing for each area, either analytically or numerically.

Factors such as geology, engineering, and economic analysis must be considered. For instance, optimal well spacing and completions design for a geologically noisy and complex reservoir will be invalid in a discreet and quiet area. Similarly, if well spacing and completions design were developed for a high-commodity-pricing environment, performing the same work flow and evaluation at lower commodity pricing would yield an increase in well spacing. The work flow described in the complete paper addresses all these factors and uses modeling, numerical simulation, ML, and linear programming to optimize NPV.

Work Flow

The work flow begins with upscaling—the process of applying a technique known as residual optimization to shrink the log into 5 to 30 layers or more, depending on heterogeneity and complexity. Often, either the permeability/porosity ratio or porosity is used to characterize reservoir heterogeneity in the Marcellus Shale. If log data are not available from a pad, various supervised ML techniques can be used to train a model to create synthetic logs and automate log projection and lithology classification. These trained models have increased the pace at which projects can be completed substantially.

Additional steps in the work flow include building a base-case model in preparation for history matching, performing a sensitivity analysis to understand the parameters that have the highest effect on production performance, and finding a range of distributions for each parameter. History matching is then performed by iterating on both uncertain parameters and those that have shown a higher effect on production performance. The authors have defined the following parameters as uncertain, and typically use them to history match Marcellus Shale assets:

  • Matrix permeability multiplier and exponent (from the permeability/porosity correlation).
  • Fracture half-length multiplier for each well. Because a distribution of fracture half-length is introduced in the base model, a fracture half-length multiplier of 1 is applied to that distribution for each well.
  • Hydraulic fracture permeability. If the completions design on all the wells within the same pad is the same, one fracture permeability will be applied across all wells. If not, a fracture permeability is applied to each well for history matching.
  • Hydraulic fracture width. The same logic that was applied to hydraulic fracture permeability is applied to fracture width.

Assisted history matching, using ML and optimization algorithms, is used to obtain the best quality and remove human bias. After obtaining a desired history match, the economic and sensitivity analysis is the last step in the process. The work flow can be completed in 1–2 weeks for a six-well pad scenario in the operator’s Marcellus Shale unit.

Case Study

Two pads located in proximity to each other in the Marcellus were modeled using the work flow described in the paper. The average pore pressure was determined to be approximately 0.64 psi/ft from a diagnostic fracture injection test, and the reservoir quality was similar for both pads. The Marcellus is known for having a large natural fracture system that can be confirmed by pressure-dependent leakoff signature on a G-function plot. The first pad (two wells) used a base completion design and the second (two wells) pumped a modified design. The interlateral spacing between all wells are at well spacing C (A being the smallest spacing and C being the largest). To understand the effect of a larger proppant/ft (proppant/cluster) design on the production performance of the wells, and to optimize well spacing, these two pads were modeled following the work flow outlined below.

  1. Using available petrophysical log data, the parameters were upscaled into 12 distinct layers to represent the reservoir precisely. The residual optimization algorithm was used, honoring all geological boundaries, along with the permeability/porosity correlation for grouping. Bulk density, porosity, water saturation, Langmuir volume and pressure, reservoir pressure, and thickness were included.
  2. The base model for both pads was created following the work flow. The full length of the well was modeled to accommodate the unbounded regions of each well.
  3. Matrix permeability multiplier and exponent, fracture half-length multiplier for each well, and fracture conductivity were used to history match both pads. The percentage error of actual as opposed to history match was less than 5%, providing confidence in the history-match quality. More than 2,000 cases were run. This process was completely automated, and time was spent only adjusting parameters.
  4. After obtaining a calibrated model for each design, six models were generated using well spacings A, B, and C for base and modified completions design over 50 years. As can be seen from Fig. 1, B and C well spacing with the modified design yielded the highest estimated ultimate recovery among the scenarios.
Fig. 1—Cumulative gas volume vs. time for various completions using design and well spacing.

Economic Analysis

The last step of this study is to perform field-level economic analysis to determine optimal well spacing and completions design for the area. Additionally, field-level economic analysis will determine the optimal economic solution. The economic model presented by the authors has been automated such that the analysis at various sensitivities can be set up and run in a short period. The well spacing and completions design that would yield the highest NPV is spacing C with the modified design, respectively, for realized gas prices up to $3.25/MMBTU, but the optimal well spacing decreases to spacing B with the modified design at $3.50/MMBTU gas pricing.

Conclusions

  • This paper illustrates a repeatable work flow to determine optimal well spacing and completions design that can be applied to various unconventional basins.
  • Normalizing the lateral length, using ML techniques, and automating the process makes it possible to calibrate a full pad in as little as 1 week.
  • The production data show that wider fracture half-lengths can be achieved by pumping more proppant and placing the wells farther apart.
  • As pricing increases, tighter well spacing is justified, illustrating the importance of reconsidering well spacing and completions design when there is a drastic change in pricing.
  • As CAPEX increases, wider well spacing is favored when compared with the base model.
This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 191779, “A Fast-Paced Work Flow for Well Spacing and Completions Design Optimization in Unconventional Reservoirs,” by Hoss Belyadi, SPE, and Malcolm Smith, EQT Corporation, prepared for the 2018 SPE Eastern Regional Meeting, Pittsburgh, Pennsylvania, USA, 7–11 October. The paper has not been peer reviewed.

Optimization Work Flow Maximizes Value of Unconventional Fields

01 October 2019

Volume: 71 | Issue: 10

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