How can you improve well placement in a horizontal well?

The horizontal part of the horizontal design needs more thought: How long and towards which direction? This is driven by purpose and utility. In tight rock and shale this reflects preferential stress direction, land limits, and ideal spacing. That also depends on timing-wells with more than a year of production cause unwanted interference to and from newer “child” wells that need to be placed further out. In conventional fields horizontal wells can fit later into injection patterns and that needs to be taken into account in a long term reservoir management plan. The same with gas storage fields. Natural fracture mapping is s key element for horizontal well design. The landing level of the horizontal well is a different subject.

With new data integration methods, merging different data sources in one digital platform becomes more visible to understand the reservoir's boundaries and capabilities. Merging seismic data with offset drilling and production data will allow better positioning and steering of the laterals inside the reservoir.

To improve well placement in a horizontal well, you can use machine learning and optimization techniques. Machine learning can analyze and model the reservoir data and performance. Optimization can search for the best well placement configuration that will maximize the objective function. You can use different types of machine learning and optimization algorithms depending on your data and problem. You can also account for uncertainty and risk, update your model and solution, and compare and evaluate different methods and results.

Conduct a thorough reservoir characterization to understand the geological and petrophysical properties of the formation. This includes porosity, permeability, rock type, and fluid properties.

Considering the conventional fields, I believe that the reservoir exploitation needs to be consider as a whole, i.e., waterflooding or, eventually, gas injection needs to be considere since the beginning of the reservoir development. Related to natural fracture mapping, I would like to know what is a reliable methodology, tool, or integrated methodology and tool to do that.

Geosteering tools evolution has evolved tremendously during the past couple of years. However the principle of each tools and what it actually measures must be properly understood to allow fit for purpose utilization in terms of cost versus benefit. This also depends on available data such as resistivity, GR, Neutron-Density, Sonic to allow for edge boundary and roof-base simulation based designation to properly geosteer and geostop within the pay zone.

Use SLBs advanced Geosteering suite which have been around for many years: Geosphere, Periscope Edge ( Deep Azim RES ) NMR, Neutron Density etc which will give you lookahead bedboundaries , borehole Imaging and RT Dip data. Also get an accurate reservoir mapping model prior to drilling and update in RT with the Geosteering team ( Petrel ) Use advanced RT Survey tools or RT LWD survey for precise TVD control for landing out and accurate steering( TRUlink ) Also try to find an experienced DD if you can

In my experience, using edge technologies like imaging tools and testing while drilling tools proved some good results. Now, merging all measurements and longing data while drilling may allow better trajectory control and keep the hole always on the high productivity side of the reservoir. Tools exist under different commercial names and different providers as well.

In absence of reliable seismic data Geosteering is challenging but very important. In a recent study in the Permian it was found that more than half of the wells are not as good as they could be. Being out of zone is more frequent that we would like to be. Having accurate information in time to make a steering change could be more valuable than drilling fast and cheap. Casing misplacement is not desirable at any means and that is another reason to steer carefully. Geologic mapping is always imprecise and continuous evaluation of cuttings makes great wells happen. There are techniques that focus on the analysis of the native hydrocarbons that make most impact.

Use more than bulk Gamma - using bulk gamma isn’t geosteering, it’s log just correlation and that was done decades before I did my Stratasteer course at Sperry in 2005. You need atleast resistivity in the hole and Azimuthal gamma would be awesome. Least then you’d know you were close to a bed boundary before you drill through them.

In order to evaluate well performance we require base line data which is not simulation derived. Such as long term production test (minimum 6 months with downhole and surface data) with surveillance data including tracers to know the heel-to-toe performance. If possible inflow control aid tools such as AICD/AICV will be helpful to deter unwanted fluids i.e. water and gas. can be included on the lower completion part - this is helped by proper zonal isolation such as swell packers.

The directional driller has is reservoir’s first responder and the first to know if something important is encountered. All these are now available in multi stream input through the mud or by a memory chip and can be analyzed in real time. To make a well as good as it can be it takes integration of data: Prognostic models, real time analysis, and on time decisions. Most important is the integration of people in the field and the expert desk. The integration plan that sometimes spans across continents and time zones is not just equipment and data, but a commitment to be present and follow through each challenge as it arises.

Use logging-while-drilling (LWD) and measurement-while-drilling (MWD) tools to gather downhole data in real-time. This information helps geologists and engineers make informed decisions abouthe wellbore trajectory.

Corrective actions are indicated, but none are proposed. Using Radial Drilling (Mechanical and directionally controlled) you can insert large bore (2-3/4") channels, up to 25m long, into the "missed" or "bypassed" optimum production zones from the motherbore. This can correct fault crossings that lose a reservoir, or sub-optimal well placement that missed the "sweet spot". It can also cross baffles or barriers above or below the motherbore to access otherwise isolated production layers. The channels not only provide access to bypassed optimal production zones, but also place an unrestricted production conduit back to the production tubing. In short..if you miss a zone that would have benefitted your production..you can now recover it.

Track the well performance at different phases pre-drill, while drilling using real time data, post drilling with memory data, while production at different times, the first phase will help you plan inflow control devices (ICD), the second two phases will help you optimize and finalize ICD design and the last phase will help you to calibrate the design and optimize future wells. use digital twins and nodal softwares to help you understand better

Analyzing various operators for long time, I can distinguish fast and slow learners from the production results. It takes more than 100 datapoints to establish a trend, but the story is compelling; given a similar position, a similar technology and geological target, different operators deliver different results and different learning curves. Field experimentation works, but it is generally a very slow and costly improvement path. Using first principles and prognostic modeling helps in accelerating the learning path. Having an agile integrated team that does it all with real time feed outperforms the competitive field.

Technology is a key enabler, as an operator leverages the existing and recent technology will help you to maximize potential and minimize the cost on the long run as well as help technology providers to improve the design

Establish a feedback loop where drilling and reservoir performance data are continuously analyzed. This information can be used to refine future well placement strategies and optimize drilling programs

Improving well placement involves a systematic approach that integrates past experiences into future planning. By updating reservoir models, well trajectories, target zones, and completion designs based on performance evaluation, you ensure a dynamic and informed strategy. Documentation of best practices, challenges, solutions, and recommendations fosters continuous improvement and knowledge sharing. Benchmarking against industry standards and peer wells provides valuable insights, guiding refinements in well performance and reservoir development. The emphasis on continuous monitoring and optimization reflects a commitment to adaptability and efficiency in the evolving field of well placement.

The determination of optimum well placement is one of the key issues in the development of oil and gas fields, whether it is the development of green fields or the management of brown fields. Many intelligent algorithms are used in automatic well location optimization: including simulated annealing algorithm, genetic algorithm, covariance matrix adaptation evolution strategy, and particle swarm optimization algorithm. One of the challenge is reducing the optimization time and improving the optimization effect to promoe the application of automatic optimization technology.

Deploying digital tools is not as easy as it sounds. Relying on a data feed that appears precise, is not a good practice. Being accurate takes more effort, cross validation and qualitative checks with domain experts. AI can be very useful if it is deployed in a practical and realistic manner. It becomes easier if the data are available and easy to pre-process. If expensive instrumentation and complex processing is needed the digital advantage fades out quickly. Having the same data bandwidth at the rig floor as the one we have in our home office sometimes is wishful thinking, but an important leap forward.

To use digital technologies and tools to improve well placement in a horizontal well, you need to consider the benefits and challenges of digital transformation in the oil and gas industry. Digital transformation is the process of using digital technologies and tools to change the way an organization operates and delivers value to its customers and stakeholders. You need to adopt a holistic and strategic approach that considers the following aspects: - Vision and leadership - Data and analytics - Technology and innovation - People and skills - Process and governance

Embracing digital technologies and tools in well placement marks a transformative shift toward enhanced capabilities and efficiency. The incorporation of artificial intelligence, machine learning, data analytics, and other digital tools allows for the seamless integration and processing of vast datasets. This empowers the generation and testing of multiple scenarios and alternatives, optimizing decision-making processes. Automation streamlines tasks, while the use of digital twins provides a virtual representation for in-depth analysis. The result is not only improved operational efficiency but also a facilitation of collaboration and communication within the well placement process, ultimately driving innovation and precision in the field.

At times completion techniques modification can be done if we have compromised the proper well placement ie isolating certain part of the horizontal section. Understanding current saturation and contact movement in brownfield is important for horizontal well placement. Length of the horizontal section must be determined based on reservoir properties and production profile required. Stress direction is important for wellbore stability during drilling and if we require hydraulic fracturing. For reservoirs with natural fractures/karst/vugs one need to decide whether to hit them or avoid them.

Innovation and experimentation are pivotal in advancing well placement strategies beyond conventional approaches. Embracing creativity and open-mindedness allows exploration and adoption of cutting-edge technologies, techniques, and solutions. Testing and validating assumptions and hypotheses become integral, fostering a culture of learning from both failures and successes. The adaptability to iterate well placement strategies enables responsiveness to evolving market dynamics and reservoir conditions. Being proactive in seeking opportunities and staying attuned to changes ensures a forward-looking approach, positioning well placement endeavors to thrive in a dynamic and challenging environment.

Horizontal well placement at high level should consider where it is placed structurally (Geosteering) and within the reservoir unit where certain reservoir quality must be met (Property Steering). Previous wells drilled nearby can be used as calibration point or pilot hole could be drilled before the horizontal well placement. LWD/MWD/PWD can be used to improve well placement. Focus also must be put on dynamic data from nearby wells ie actual production, saturation logs, SBHP surveys etc to ascertain that our static reservoir characteristics understanding when placing the horizontal well is matched/calibrated with actual dynamic well/reservoir performance.

The key in getting improved PI and less skin is in the drilling-completion fluid selection within the pay zone. Also the degree of overbalance - at balance - under balance of the fluid weight. Filter cake and filter cake breaker selection is also key in getting improved production. 2 ways of removing cake is via mechanical (scraper/wiper trip) or via chemical - hence Lab test coupled with proper chemistry modeling within the wellbore geometry interaction, placement method, soaking time also will be key success factors.

The degree of importance of optimum wellbore and fluid exit placement (elevation, lateral spacing, and cluster spacing) is related to the size of the proposed acreage to be developed. The consequences of “getting it wrong” for a small project (i.e., 160 acres, 1 depth target) are relatively small compared to the overall D&C costs. But, say, a +100K acre proposed development scenario with one or more depth targets——it’s a whole different ballgame. Optimizing parallel lateral and cluster spacing in this scale can quickly morph into a billion-dollar issue from a hydrocarbon recovery standpoint. Parameter measurements, modeling, DOE, verification, and validation then become critical pieces in maximizing NPV.

Consider a case where a petroleum engineer implemented advanced reservoir modeling techniques, integrating real-time data from sensors and IoT devices. This approach allowed for dynamic adjustments to well placement strategies based on constantly evolving subsurface conditions. In another instance, a team utilized a digital twin to create a virtual replica of an oilfield, enabling them to simulate and optimize well trajectories before actual drilling. This not only reduced the likelihood of costly errors but also enhanced the precision of well placement. Moreover, an oil and gas company successfully leveraged AI algorithms to analyze historical well placement data and identify patterns that traditional methods had overlooked.