What are the risks of overestimating or underestimating production in forecasting?

Overestimating or underestimating production can lead to financial and operational risks. Overproduction causes excess inventory, increasing carrying costs, reducing profitability, and tying up capital. Underestimating production leads to stockouts, increased lead times, production bottlenecks, loss of market share, and loss of customer trust. Accurate forecasting is crucial for optimizing production levels, managing inventory, and ensuring customer satisfaction.

overestimating production forecasts can lead to increased cost, excess inventory and sometimes over utilization of assets. This may result in financial losses if the products cannot be sold as anticipated.

This will be a paid mistake as all the results later will be done as previous calculations and the resources of the company will have to deal with that making more or less costs and more or less workforce etc. and all goes under less or more OPEX.

When companies pursue organic growth, new drilling is funded by cash flow and if production targets are missed, there will be less capital available to support planned drilling.

From experience; balancing production forecasts is essential to avoid distinct risks. Overestimation may cause excess inventory, capital tie-up, and obsolescence, impacting profitability. It strains resources, leading to markdowns. Underestimation risks stockouts, affecting customer satisfaction and market share. It strains supply chains, causing rushed production and higher costs. Both errors hinder planning accuracy, impacting decision-making. Overestimating misses opportunities, while underestimating incurs excessive expenses. Striking a balance is crucial for optimal resource use, cost management, and customer trust. Regular reassessment and data analytics enhance adaptability in dynamic markets. Thanks

Moreover, fostering collaboration between interdisciplinary teams, including geologists, reservoir engineers, and environmental experts, can provide a holistic understanding of the production landscape. This integrated approach enables drilling engineers to factor in diverse considerations, such as environmental sustainability and social responsibility, ensuring a more balanced and responsible forecasting strategy that aligns with broader industry trends and expectations.

Uma das causas de se subestimar a produ??o de petróleo é a escassez de suprimentos; consequentemente, aumento dos pre?os do petróleo, instabilidade econ?mica e impactes ambientais - o pouco investimento em fontes alternativas de energia pode afectar de forma significativa porque a transi??o para fontes mais limpas pode ser comprometida, incluindo todo o esfor?o de sustentabilidade.

From the point of view of National Oil Companies, production forecasts are essential in terms of potential future revenues for the National budget and taxes to be collected from oil and gas companies operating in the country. It's also a guarantee for stakeholders (World Bank) to have an idea of the solvency of the STATE in terms of payment of its debts. In countries where the STATE is dependent on oil and gas exports, the Central Bank needs production forecasts in order to provide the expected foreign currency exchange, since crude oil is traded in US Dollars. It is therefore imperative that production estimates are accurate and realistic to avoid any misinterpretation of revenues by all partners in an oil and gas joint venture.

Furthermore, with the advent of data-driven technologies, machine learning models are increasingly being employed for production forecasting. These models leverage vast datasets to identify intricate patterns and dependencies, offering a more dynamic and adaptive approach. By embracing machine learning, drilling engineers can enhance their forecasting accuracy, especially in situations with evolving reservoir conditions or unconventional production scenarios, providing a valuable complement to traditional analytical methods.

Also Reservoir simulation studies can de used as a part of 4D Geomechanical studies, to predict impact of field development on wellbore stability and formation pressure, that is important to plan drilling operations.

Moreover, as the energy industry undergoes transitions and embraces sustainable practices, there's a growing need to integrate environmental and social considerations into production forecasting. Sustainable forecasting entails evaluating the environmental impact of production methods, considering community engagement, and aligning projections with evolving regulatory frameworks. By incorporating these dimensions, drilling engineers can contribute to responsible resource management and enhance the industry's resilience in a changing global landscape.

simulation,forcasting ,tools are for increase recovery factor selection wells posotion and well directional or multilateral and ior senaroous iare impoerant but static modling and reseevoir dara haveto estimated correct ..

Furthermore, fostering a culture of continuous learning and adaptation is crucial. Drilling engineers should actively seek opportunities to update their skills, staying abreast of technological advancements and industry trends. Embracing a mindset of adaptability allows for the incorporation of innovative techniques and tools into forecasting practices, ensuring that methodologies remain cutting-edge and responsive to evolving challenges in the dynamic field of energy production.

In nowadays, by my experience most of fields development operations based on 3D geological model with forecast on reservoir simulators (Eclipse, T-Navigator, Intersect), that used from planning development, pre- and actual Geosteering operations to decisions which well completions equipment should be used (ESP, Frac, screen etc.)

Having a drilling engineer to forecast well productivity contributes to the risks. Better to leave it to reservoir/production engineers.

for forcasting we need actual data . usually reservoir simulation & optimaization drillng method . selection senarious production is important and ior method